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<title>Replication Output for: Can the Government Deter Discrimination? Evidence from a Randomized Intervention in New York City</title>

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m5ABkAAfS4AAUQuQAjAAH0MDE3ND4CMzIeAhUUDgIjIi4CNxQeAjMyPgI1NC4CIyIOAjQjO04rK087IyM7TysrTjsjLhotPiQkPy0aGi0%2FJCQ%2BLRrvPF9AIiJAXzw8XUAiIkBdPC9OOB8fOE4vME45Hx85TgAAAgBc%2FycB7AHsABQAJQCDALgAAEVYuAAJLxu5AAkACD5ZuAAARVi4AAMvG7kAAwAIPlm4AABFWLgAAi8buQACAAY%2BWbgAAEVYuAARLxu5ABEABD5ZugAFAAkAERESOboAFAARAAkREjm4ABQQuQAVAAH0uAARELkAGAAB9LgACRC5ACIAAfS4AAUQuQAlAAH0MDEXFSMRMxczPgEzMhYVFA4CIyImJzUeATMyPgI1NC4CIyIGB4gsJgQDI1MtYGAiO00qIkgmKkgcJD4sGREkOikkTSwqrwK5PBwshXA9YEMjHxwqIhwgOlExLEw3HykmAAAAAAIANP8nAcQB7AAUACUAfwC4AABFWLgABS8buQAFAAg%2BWbgAAEVYuAALLxu5AAsACD5ZuAAARVi4AA0vG7kADQAGPlm4AABFWLgAEi8buQASAAQ%2BWboACAAFABIREjm6AA8AEgAFERI5uQAaAAH0uAAPELkAHQAB9LgACBC5AB4AAfS4AAUQuQAhAAH0MDE3ND4CMzIWFzM3MxEjNTcOASMiJjcUHgIzMjY3ES4BIyIOAjQiO00rLEEiAgUlLAIgTzBbbC8VKDsmKEkmJkMjJD4tGu86XkIjHhos%2FUe5XR0sgXowTjcfKSYBHSIcITlPAAEAXAAAAUEB7AASAFIAuAAARVi4AAYvG7kABgAIPlm4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAASLxu5ABIABD5ZugACAAAAEhESObgABhC4AA3cuAACELkAEAAB9DAxEzMXMz4BMzIWFwcuASMiBgcRI1wmBAMYRSsOFgwKDBIOIEccLAHgWS04BAYoBQM3RP65AAABACD%2F9AFyAewAMwBJALgAAEVYuAAVLxu5ABUACD5ZuAAARVi4ADAvG7kAMAAEPlm5AAMAAfS6AAsAMAAVERI5uAAVELkAHAAB9LoAJgAVADAREjkwMTceATMyNjU0LgInLgM1ND4CMzIWFwcuASMiDgIVFB4CFx4DFRQOAiMiJic6H0Y0OTkVIikVGzcsGxQoOSYmSRoYGDYlHCgbDRMgKRUcOC0cFSo8JzZaIFcaIzkmFiIYEggKFh8rHxovIxQcFh8SGQ8YIBAVHRYRCAsWIC8jGzElFicbAAAAAQAc%2F%2FQBLQJrABsARQC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAWLxu5ABYABD5ZuAAGELkACQAB9LgAANC4AAYQuAAD0LgAFhC5AA8AAfQwMRMjNT8BMxUzFSMRFB4CMzI2NxcOASMiLgI1Z0tMBiaLiwcSHxkOIQ0MFSoPIy4cCwG6IgSLiyb%2BxxclGw8JBiQIChUmNiAAAAAAAQBV%2F%2FQBtQHgABQAZQC4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAAJLxu5AAkACD5ZuAAARVi4ABEvG7kAEQAEPlm4AABFWLgADC8buQAMAAQ%2BWbgAERC5AAUAAfS6AA0ACQAMERI5uAANELkACAAB9DAxEzMRFBYzMjY3ETMRIycjDgEjIiY1VSwzOitGKiwlBQIjTjJLRgHg%2FtJNSS8zAWL%2BIFAqMlpeAAAAAAEADAAAAaYB4AANAEAAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgACi8buQAKAAg%2BWbgAAEVYuAANLxu5AA0ABD5ZugAFAAAADRESOTAxEzMTHgEXMz4BNxMzAyMMMGwLGgsEDBkMbC2yNAHg%2FtMjRiEhRiMBLf4gAAEAGAAAApUB4AAhAHYAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgACi8buQAKAAg%2BWbgAAEVYuAAULxu5ABQACD5ZuAAARVi4ACEvG7kAIQAEPlm4AABFWLgAFy8buQAXAAQ%2BWboABQAhAAAREjm6AA8AFwAUERI5ugAcAAAAIBESOTAxEzMTHgEXMz4BNxMzEx4BFzM%2BATcTMwMjAy4BJyMOAQcDIxgwVgkQCAQIEglXNVcJEgkECBIIVS2KOlQLDwsECBMLUzUB4P7JIT8gID8hATf%2BySE%2FICA%2FIQE3%2FiABKiNDIyNFI%2F7YAAAAAQAOAAABiQHgABkAWwC4AABFWLgAAS8buQABAAg%2BWbgAAEVYuAALLxu5AAsACD5ZuAAARVi4ABkvG7kAGQAEPlm4AABFWLgADy8buQAPAAQ%2BWboABgALAA8REjm6ABQAAQAZERI5MDE3JzMXHgEXMz4BPwEzBxcjJy4BJyMOAQ8BI7GWMU4MGQ4EDRcNSy6VozFVDhwPBA4bDlIv%2B%2BV6FCcUFCcUeun3gxcsFRUsF4MAAAAAAQAM%2FyUBqAHgABwARgC4AABFWLgACC8buQAIAAg%2BWbgAAEVYuAASLxu5ABIACD5ZuAAARVi4ABkvG7kAGQAGPlm5AAMAAfS6AA0ACAAZERI5MDEXHgEzMjY%2FAQMzEx4BFzM%2BATcTMwMOAyMiJzciCBQLLTwSDcUwdAsbDgQLFwpnLb4KHSczIBsWCqoDBUg3KgHp%2FtIeSCAgSB4BLv3kHjktGwonAAAAAQAbAAABegHgAAkAPQC4AABFWLgAAy8buQADAAg%2BWbgAAEVYuAAILxu5AAgABD5ZuQAGAAH0uAAA0LgAAxC5AAEAAfS4AAXQMDE3ASM1IRUBIRUhGwEc%2FQE2%2FuUBJf6hGAGiJhf%2BXicAAAD%2F%2FwAIAAACBgMvAiYABAAAAAcA4QEHAAD%2F%2FwAIAAACBgMvAiYABAAAAAcA4wEHAAD%2F%2FwAIAAACBgMvAiYABAAAAAcA5QEHAAD%2F%2FwAIAAACBgMoAiYABAAAAAcA5wEHAAD%2F%2FwAIAAACBgMeAiYABAAAAAcA6gEHAAD%2F%2FwAIAAACBgNgAiYABAAAAAcA7AEHAAAAAgAVAAAC%2BgKTAAYAFgB8ALgAAEVYuAAOLxu5AA4AED5ZuAAARVi4AA0vG7kADQAEPlm4AABFWLgACS8buQAJAAQ%2BWboAAgAOAA0REjm6AAoADgANERI5uAAKL7kABgAB9LgACRC5AAcAAfS4AA4QuQAQAAH0ugAVAA4ACRESObgAFS%2B5ABMAAfQwMSURIw4BDwEFFSE1IwcjASEVIRUzFSMRAZsEHz0gSQIo%2FqHedzEBbQFu%2Ftn19f8BbDhzPIXXKNnZApMo%2BSj%2B3gD%2F%2FwA3%2FzACDwKfAiYABgAAAAcA7gFTAAD%2F%2FwBhAAAB1AMvAiYACAAAAAcA4QEaAAD%2F%2FwBhAAAB1AMvAiYACAAAAAcA4wEaAAD%2F%2FwBhAAAB1AMvAiYACAAAAAcA5QEaAAD%2F%2FwBhAAAB1AMeAiYACAAAAAcA6gEaAAD%2F%2FwAHAAAAnQMvAiYADAAAAAYA4XgAAAD%2F%2FwBTAAAA6QMvAiYADAAAAAYA43gAAAD%2F%2FwADAAAA7QMvAiYADAAAAAYA5XgAAAD%2F%2FwAFAAAA6wMeAiYADAAAAAYA6ngAAAD%2F%2FwBhAAACGwMoAiYAEQAAAAcA5wFDAAD%2F%2FwA3%2F%2FQCVgMvAiYAEgAAAAcA4QFGAAD%2F%2FwA3%2F%2FQCVgMvAiYAEgAAAAcA4wFGAAD%2F%2FwA3%2F%2FQCVgMvAiYAEgAAAAcA5QFGAAD%2F%2FwA3%2F%2FQCVgMoAiYAEgAAAAcA5wFGAAD%2F%2FwA3%2F%2FQCVgMeAiYAEgAAAAcA6gFGAAAAAwA4%2F%2BkCWQKqAAsAFwAzAIUAuAAARVi4AC4vG7kALgAQPlm4AABFWLgAIC8buQAgAAQ%2BWboAAAAgAC4REjm5AAMAAfS6AAsALgAgERI5ugAMAC4AIBESObgALhC5AA8AAfS6ABcAIAAuERI5ugAYAC4AIBESOboAIwAgAC4REjm6ACYAIAAuERI5ugAxAC4AIBESOTAxNx4BMzI%2BAjU0Ji8BLgEjIg4CFRQWFwEeARUUDgIjIiYnByc3LgE1ND4CMzIWFzcXsR1NLzJSOyAbGRcdSi0yUzogGRcBdyIlKEhkOzZcI0EcRiAkKEhkPDRYIz0dZSIlK09vRT9mJh4fISpNbkQ9ZSYBuSx6S05%2FWjEnJVcWXi14Sk59WS8kIlEWAAAAAAIANwAAAxICkwAUACEAVQC4AABFWLgABS8buQAFABA%2BWbgAAEVYuAAQLxu5ABAABD5ZuAAFELkAHAAB9LgAB9C6AAwABQAQERI5uAAML7kACgAB9LgAEBC5ABsAAfS4AA7QMDETND4CMyEVIRUzFSMRIRUhIi4CNxQeAjsBESMiDgI3J091TgGY%2Ftn19QEx%2Fl1OdU4nMCBDZUU%2FP0VlQyABTEt4Vi4o%2BSj%2B3igvV3tLQWtOKwJFKktrAAD%2F%2FwBf%2F%2FQCGwMvAiYAGAAAAAcA4QE9AAD%2F%2FwBf%2F%2FQCGwMvAiYAGAAAAAcA4wE9AAD%2F%2FwBf%2F%2FQCGwMvAiYAGAAAAAcA5QE9AAD%2F%2FwBf%2F%2FQCGwMeAiYAGAAAAAcA6gE9AAD%2F%2FwADAAABvAMvAiYAHAAAAAcA4wDfAAAAAgAlAAACOgKTABAAIQBZALgAAEVYuAAhLxu5ACEAED5ZuAAARVi4ABwvG7kAHAAEPlm5AAAAAfS4ACEQuQAKAAH0ugAOACEAHBESObgADi%2B5AA0AAfS4AA4QuAAe0LgADRC4AB%2FQMDElMj4CNTQuAisBFTMVIxETMh4CFRQOAisBESM1NxEBCkNgPx4eP2BDZqOjakxxSyQkSnFMmVFRJytOa0FAakwq%2FyH%2B2wJsLlZ4S0t7Vy8BTB8CASYAAAAAAgBhAAACAAKTAA4AFwA5ALgAAEVYuAAALxu5AAAAED5ZuAAARVi4AA4vG7kADgAEPlm7ABcAAQAMAAQruwACAAEAFgAEKzAxEzMVMzIeAhUUBisBFSM3MjY1NCYrARFhLoY3Vz0gfG%2BGLqllYmNkewKTdRQrRjNhX6bNSFFSP%2F7WAAD%2F%2FwBB%2F%2FQBowLEAiYAHgAAAAcA4AEGAAD%2F%2FwBB%2F%2FQBowLEAiYAHgAAAAcA4gEGAAD%2F%2FwBB%2F%2FQBowLEAiYAHgAAAAcA5AEGAAD%2F%2FwBB%2F%2FQBowKeAiYAHgAAAAcA5gEGAAD%2F%2FwBB%2F%2FQBowKfAiYAHgAAAAcA6QEGAAD%2F%2FwBB%2F%2FQBowLIAiYAHgAAAAcA6wEGAAAAAwBB%2F%2FQC6gHsABAASQBSAJsAuAAARVi4ABYvG7kAFgAIPlm4AABFWLgAHC8buQAcAAg%2BWbgAAEVYuAA5Lxu5ADkABD5ZuAAARVi4ADEvG7kAMQAEPlm4ADkQuQADAAH0ugBBADkAFhESObgAQS%2B5AAwAAfS6ACIAHAAxERI5uAAiL7gAMRC5ACgAAfS4ABYQuQBGAAH0uAAcELkATQAB9LgAIhC5AFIAAfQwMTcUFjMyPgI3LgE1Jw4DAz4DMzIWFz4BMzIWFRwBByEUHgIzMjY3Fw4DIyIuAicOASMiLgI1NDY3NC4CIyIGBwU0JiMiDgIHbzwpEy4vLhULCgFGYj4cFg0kLTMbO0UMGlc2VV0C%2FrQaLj8kJzsbEw4eJCoaIDQqIQ0wbS0dMicWlpsIGS0jLU8YAlRJQR41KRsEejQsDBgjFhZBIhsJGiQuARgJFhINQTc3QXRqCRIJLUw3HxcUIwkQDggRHCMTLjUQIDEiUFQSGzcrGyYSgV1dGzFFKQAA%2F%2F8ANP8wAacB7AImACAAAAAHAO0BCQAA%2F%2F8ANP%2F0Ab0CxAImACIAAAAHAOABBQAA%2F%2F8ANP%2F0Ab0CxAImACIAAAAHAOIBBQAA%2F%2F8ANP%2F0Ab0CxAImACIAAAAHAOQBBQAA%2F%2F8ANP%2F0Ab0CnwImACIAAAAHAOkBBQAA%2F%2F8AGgAAAKoCxAImAGcAAAAGAOByAAAA%2F%2F8AOgAAAMoCxAImAGcAAAAGAOJyAAAA%2F%2F8AAAAAAOQCxAImAGcAAAAGAORyAAAA%2F%2F%2F%2F%2FwAAAOUCnwImAGcAAAAGAOlyAAAAAAEAXAAAAIgB4AADACUAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgAAi8buQACAAQ%2BWTAxEzMRI1wsLAHg%2FiAAAP%2F%2FAFwAAAG%2FAp4CJgArAAAABwDmARsAAP%2F%2FADT%2F9AHjAsQCJgAsAAAABwDgAQsAAP%2F%2FADT%2F9AHjAsQCJgAsAAAABwDiAQsAAP%2F%2FADT%2F9AHjAsQCJgAsAAAABwDkAQsAAP%2F%2FADT%2F9AHjAp4CJgAsAAAABwDmAQsAAP%2F%2FADT%2F9AHjAp8CJgAsAAAABwDpAQsAAAADAC7%2F6gHpAfUACQATAC4ASQC4AABFWLgAKS8buQApAAg%2BWbgAAEVYuAAcLxu5ABwABD5ZugAAABwAKRESObkAAgAB9LoACgApABwREjm4ACkQuQAMAAH0MDE3FjMyPgI1NC8BJiMiDgIVFBcBHgEVFA4CIyInByc3LgE1ND4CMzIWFzcXmS9DJD8uGiQULkUkPy0aJAEqGBwjO08rUjs2GjoYHCM7TisoSR02GkwyHzhOL1I4GzMfOU8vUTgBNCBWNjxdQCI4QhVGIFQ2PF9AIhwcQRUAAAADADT%2F9AMrAewAEwA7AEQAcQC4AABFWLgAGS8buQAZAAg%2BWbgAAEVYuAA3Lxu5ADcABD5ZuQAFAAH0uAAZELkADwAB9LgAGRC4AB%2FQuAA3ELgAMdC6ACQAHwAxERI5uAAkL7gAMRC5ACoAAfS4AB8QuQA%2FAAH0uAAkELkARAAB9DAxNxQeAjMyPgI1NC4CIyIOAgc0PgIzMhYXPgEzMhYVFAchFB4CMzI2NxcOASMiJicOASMiLgIlNCYjIg4CB2IZLD0jJD0rGRkrPSQjPSwZLiI7TCo6ZhoaXzpVYgP%2BrxsuQCQnPhsTHUU2P2QaHGA%2FKkw7IgLNTUAeNiobBO8vTjgfHzhOLzBOOR8fOU4wPF9AIkdHQkx0ahISLUw3HxcUIxEeTEFGRyJAXVldXRsxRSkAAAEAXP%2F0AgEC2QA5AGQAuAAARVi4AAMvG7kAAwASPlm4AABFWLgAOS8buQA5AAQ%2BWbgAAEVYuAAZLxu5ABkABD5ZugANABkAAxESObkAIAAB9LoAJQAZAAMREjm6ADEAGQADERI5uAADELkANAAB9DAxEzQ2MzIeAhUUDgIVFB4EFRQOAiMiJic3HgEzMj4CNTQuBDU0PgI1NCYjIgYVESNcXU4hMyQTHyUfHy82Lx8WJzUgJ0QdFh01IBkmGQ0fLjcuHx8kHy8wOUUsAhRdaBUkMRwnNy8vHh0kGxokNioeMiYVHBchFxcRGyQTIywfGSAtJCQ0LzEiLDhUWv37AP%2F%2FAFX%2F9AG1AsQCJgAyAAAABwDgAQgAAP%2F%2FAFX%2F9AG1AsQCJgAyAAAABwDiAQgAAP%2F%2FAFX%2F9AG1AsQCJgAyAAAABwDkAQgAAP%2F%2FAFX%2F9AG1Ap8CJgAyAAAABwDpAQgAAP%2F%2FAAz%2FJQGoAsQCJgA2AAAABwDiAOUAAP%2F%2FAAz%2FJQGoAp8CJgA2AAAABwDpAOUAAAACADz%2F9AHWAtQAFQA8AFkAuAAARVi4ADcvG7kANwASPlm4AABFWLgAIC8buQAgAAQ%2BWbsAKgABAAwABCu6ADMAMAADK7gAIBC5AAAAAfS4ADAQuAAW0LgAMxC4ADfcuAAzELgAOtAwMSUyPgI1NCcuAyMiDgIVFB4CEx4DFRQOAiMiLgI1ND4CMzIWFy4BJwcnNy4BJzceARc3FwELKTsnEwMTJyYmEyk9KhUaLDxlHjEkFB41Sy0oSzkjHjdMLitMHQ48Ko4PhBw%2BIRYlRSCODxsjPFIvIB4cJBQHHjNDJSpHMxwCRB5JV2g9PGJFJSA7VjYxUzohJiZKaipKHEQYKRMeFC4dShsAAAAAAgBc%2FycB7ALPABYAJwBXALgAAEVYuAAILxu5AAgACD5ZuAAARVi4AAIvG7kAAgASPlm4AABFWLgAAS8buQABAAY%2BWbgAAEVYuAASLxu5ABIABD5ZuQAaAAH0uAAIELkAJAAB9DAxFyMRMx0BPgEzMh4CFRQOAiMiJicVNR4BMzI%2BAjU0LgIjIgYHiCwsJFItMEkwGCI7TSojRyYqSBwkPiwZESQ6KSRNLNkDqM9bHCsjQFo4PWBDIx4cWIMiHCA6UTEsTDcfKSYAAAAAAQAiAAACKgLbACoAfAC4AABFWLgAFC8buQAUAAg%2BWbgAAEVYuAAoLxu5ACgAEj5ZuAAARVi4ABAvG7kAEAAEPlm4ACgQuQADAAH0uAAUELgAJNC4AAjQuAAUELkAEQAB9LgADdC4AAnQuAAQELgADNC4ABQQuAAT0LgAKBC4ABjQuQAfAAH0MDEBLgEjIgYdATMVIxEjESMRIxEjNTc1NDYzMhYXBy4BIyIGHQEzNTQ2MzIXAh4PHQ4mJW9vLN4sQUFEPhQpFAwSIhErK94%2BOSMjAqcIBjk0aCb%2BRgG6%2FkYBuiIEWUtNCQkkCQc9OFZrSEgQAAEAIf%2F0AhAC2wAuAIkAuAAARVi4ABAvG7kAEAAIPlm4AABFWLgAFC8buQAUABI%2BWbgAAEVYuAAMLxu5AAwABD5ZuAAARVi4AAMvG7kAAwAEPlm4ABAQuQANAAH0uAAJ0LgAEBC4AA%2FQuAAPL7gAFBC5ABoAAfS4ABAQuAAf0LgAI9C4AAkQuAAk0LgAAxC5ACsAAfQwMSUOASMiLgI1ESMRIxEjNTc1NDYzMhcHLgEjIgYdATM3MxUzFSMRFB4CMzI2NwIQFSsPIy0cC7ssQkI%2BOSMjDA8dDiYlvAYljIwHEiAZDiENBggKFSY2IAE1%2FkYBuiIEa0hIECQIBjk0aIuLJv7HFyUbDwkGAAADACT%2F9AIyAp8ADwAfAEwAjAC4AABFWLgANy8buQA3ABA%2BWbgAAEVYuAAlLxu5ACUABD5ZuAAARVi4ACAvG7kAIAAEPlm6AEIAIgADK7gAJRC5AAMAAfS6AAYAIgBCERI5ugALACUANxESObgACy%2B4ABPcuAA3ELkAHQAB9LoALwALABMREjm6AD8ACwATERI5ugBJACIAQhESOTAxNzI2Ny4BJw4DFRQeAgMUFhc%2BAzU0LgIjIgYBJicOASMiLgI1ND4CNy4BNTQ%2BAjMyFhUUDgIHHgEXPgE3Mw4BBx4BF%2BYpSh81YyMWJx4RGCg3IBQRGS4kFQgRGxQsMQF7QUwkWDgnRjMeFyUxGhQYFCQyHjY4Gis3HCNhMyM0ESsUOSgjQB0aJyAweEARJCcrGCE1JRUB9CFHJBIkKCsaEB8YD0D9uRc8JS4ZL0IpITYvKRMqUyYgNicWRDQgNi8rFT92LS1tP0R7MxwlCwAAAgAw%2F%2FQBrgKLAAsAFwA1ALgAAEVYuAAGLxu5AAYADj5ZuAAARVi4AAAvG7kAAAAEPlm5AAwAAfS4AAYQuQASAAH0MDEXIiY1NDYzMhYVFAYnMjY1NCYjIgYVFBbvXGNjXFxjY1xDT09DQlBQDK2hoKmpoKGtJpWTk5CQk5OVAAABAFQAAAGiAn8ADABDALgAAEVYuAAHLxu5AAcADj5ZuAAARVi4AAwvG7kADAAEPlm5AAEAAfS4AAcQuQAEAAH0uQACAAH0uAABELgACdAwMTczESM1PgE3MxEzFSFUlnInPRcki%2F6yJwISHgcUDf2oJwAAAAEAJwAAAbECiwAdAD0AuAAARVi4AA8vG7kADwAOPlm4AABFWLgAHC8buQAcAAQ%2BWbkAGgAB9LgAF9C4AADQuAAPELkACAAB9DAxNz4DNTQmIyIGByc%2BATMyFhUUDgIHPgE7ARUhKU92TydDSCxMHR0kVztWXipMbUIaNxra%2FngcUH9pWSo8UjAkHCg2YlEwX2l3RgIDKAAAAQAd%2F%2FQBqwKLADcAUwC4AABFWLgAHy8buQAfAA4%2BWbgAAEVYuAAyLxu5ADIABD5ZuQAFAAH0ugAPAB8AMhESObgADy%2B5ABAAAfS4AB8QuQAYAAH0ugAoABAADxESOTAxNx4DMzI%2BAjU0LgIjNTI%2BAjU0JiMiBgcnPgEzMh4CFRQGBxUeAxUUDgIjIi4CJzcOIis1ISA3KBYZNlQ8N00vFUY7LUocGiFUOCVALxtFNR40KBceNUcpJj4xJw9tEB0XDhQlNCAhNicWJxYlMx02QSkdHiAuFSc5JT9NEgQGHSo3IipDLxkQGiAQAAACABAAAAHAAn8ACQAUAFcAuAAARVi4ABIvG7kAEgAOPlm4AABFWLgADS8buQANAAQ%2BWbsADgABAAAABCu4ABIQuAAE3LgAABC4AAnQuAAOELgAC9C4AAkQuAAQ0LgAABC4ABPQMDElNTQ2NyMOAQcDBSMVIzUhNQEzETMBNgICBAwaDrwBel8r%2FtoBKShf5eoWQhYUJhb%2B%2BCa%2FvxoBpv5mAAAAAAEAGv%2F0Aa8CfwAoAEcAuAAARVi4ABIvG7kAEgAOPlm4AABFWLgAIy8buQAjAAQ%2BWbkABQAB9LoAGQASACMREjm4ABkvuAAN3LgAEhC5ABQAAfQwMTceAzMyPgI1NCYjIgYHJxMhFSMHPgEzMh4CFRQOAiMiLgInMw4iKjQhIDorGlRIJTUdHxcBJ%2F8UGTYjKEg0HyM5SScmPTEmD2kPHBYNGS0%2FJ05XFxQTASsn5w8TGDFNNDNPNxwPGR8PAAAAAAIANP%2F0AbQCiwARADAAQwC4AABFWLgALS8buQAtAA4%2BWbgAAEVYuAAlLxu5ACUABD5ZuwAdAAEACgAEK7gAJRC5AAAAAfS4AC0QuQAVAAH0MDElMj4CNTQuAiMiBgceAxMuASMiDgIHPgEzMhYVFA4CIyImNTQ%2BAjMyFhcBBhwvIhQQITUkIlInAxcoO6wVOB8mRDQfASFTLVVdHDA%2FJGFwJ0FVLi1BGRoZLDsiIjsrGCs1M1M7IQIVGhsgSnlYKTBlYSxJNR6ckmSKViUiHAABACwAAAG1An8ADwAzALgAAEVYuAAHLxu5AAcADj5ZuAAARVi4AAAvG7kAAAAEPlm4AAcQuQAFAAH0uAAJ0DAxMz4DNyE1IRUOAwcjvAQZLkYw%2Fq8BiTlLLRQEMGGhj4NEJxpMjJGeXgAAAAADACj%2F9AG1AosAEQAjAE0AVwC4AABFWLgANC8buQA0AA4%2BWbgAAEVYuABJLxu5AEkABD5ZuQAFAAH0ugAPAEkANBESObgADy%2B4ABLQuAA0ELkAGgAB9LgADxC4ACncuAASELgAPtwwMTcUHgIzMj4CNTQuAicOATc%2BATU0LgIjIg4CFRQeAgc0PgI3NS4DNTQ%2BAjMyHgIVFA4CBxUeAxUUDgIjIi4CVBgqOiMiNycVITdGJTFAzikrEiExHxstIRIdMD3aFyUtFxIhGRAaLT0jKUAtFxIbIA4VKB8UHDNIKyxKNh%2BmHjMmFhQkMBslMycdDx1PdCFJKhsvJBUSHywaIjElHLcgOC0jDAQMHSUrGiI6KRcZLT0kGjEqIgsEDR4mMiAjPS0aGy9AAAACACv%2F9AGqAosAEQAwAEMAuAAARVi4ACUvG7kAJQAOPlm4AABFWLgALS8buQAtAAQ%2BWbsAAAABAB0ABCu4ACUQuQAIAAH0uAAtELkAFQAB9DAxEzI2Ny4DIyIOAhUUHgIHHgEzMj4CNw4BIyImNTQ%2BAjMyFhUUDgIjIiYn4iJSJwMXKTsnGzAiFBAiNGoVOCAmRDQfASFULVRdHDA%2FJGBwJ0BWLi1CGAEjLTQzUzsgGSs7IiM7KxjTGhsgSnlYKDBlYStKNB6bkmWKVSYiHAAAAQBD%2F%2FQAmABPAAsAGAC4AABFWLgACS8buQAJAAQ%2BWbgAA9wwMTc0NjMyFhUUBiMiJkMaEREZGRERGiEWGBgWFRgYAAABADD%2FZQClAE8AEQAYALgAAEVYuAAFLxu5AAUABD5ZuAAL3DAxFz4BNwYjIiY1NDYzMhYVFAYHMCIqAQQIERkaERccOi19Ej0qARYUFBYmITdUGP%2F%2FAEP%2F9ACYAc0CJwCGAAABfgAGAIYAAP%2F%2FADD%2FZQClAc0CJwCGAAABfgAGAIcAAAACAFf%2F9ACsAp4ABQARACkAuAAARVi4AAEvG7kAAQAQPlm4AABFWLgADy8buQAPAAQ%2BWbgACdwwMRM1MwcDIwc0NjMyFhUUBiMiJmsuAQYhGhoRERkZEREaAldHR%2F5ckhYYGBYVGBgAAAIAV%2F9CAKwB7AAFABEAGAC4AABFWLgADy8buQAPAAg%2BWbgACdwwMR8BIzUTMzcUBiMiJjU0NjMyFpgBLgYhGhkRERoaEREZd0dHAaSTFxgYFxQYGAAAAAACACX%2F9AFgAqoAHwArACYAuAAARVi4ACkvG7kAKQAEPlm7ABMAAQAMAAQruAApELgAI9wwMTcmPgQ1NC4CIyIGByc%2BATMyHgIVFA4EFwc0NjMyFhUUBiMiJqMGESAoJBgNHCsdJUMZGx1ONCU6KBUZJCkhEwU7GRERGhoRERmzKUM5MjE0HxcrIBMhHxkhLRcpOCEiOTQzNz8mkhYYGBYVGBgAAAIAM%2F82AW4B7AAfACsAJgC4AABFWLgAKS8buQApAAg%2BWbsADAABABMABCu4ACkQuAAj3DAxExYOBBUUHgIzMjY3Fw4BIyIuAjU0PgQnNxQGIyImNTQ2MzIW8AURICgkGA0cKx4kQxkcHU8zJTooFRkkKSESBTsZEREZGRERGQEtKUM5MjE0HxcqIRMiHhkgLhcpOCEiOTUyNz8mkxcYGBcUGBgAAQBTAd0AhgK1AAUACwC6AAIABAADKzAxEyczFQcjVAEzCSECcEVFkwAAAP%2F%2FAFMB3QETArUAJgCOAAAABwCOAI0AAAABADoB4AChAr8AEQANALsABQABAAsABCswMRMOARU2MzIWFRQGIyImNTQ2N6EhIgIGDxkXERQYLSgCqBs2KwESExMVIyA2TBoAAAAAAQA6Ad0AogK8ABEADQC7AAsAAQAFAAQrMDETPgE1BiMiJjU0NjMyFhUUBgc6ISICBg4ZFhEUGS0pAfQbNisBEhMTFSMgNkwaAAAA%2F%2F8AOgHgAS4CvwAmAJAAAAAHAJAAjQAA%2F%2F8AOgHdAS8CvAAmAJEAAAAHAJEAjQAA%2F%2F8AOv9%2BAKIAXQIHAJEAAP2hAAD%2F%2FwA6%2F34BLwBdACcAkQAA%2FaEABwCRAI39oQAAAAEAKwBIAM0BsAAGAAsAugACAAYAAyswMTc1NxcHFwcriRl7exnpJqEVn6ETAAAAAAEANgBIANgBsAAGAAsAugACAAUAAyswMTcnNxcVByewehiKihj8nxWhJqETAAAA%2F%2F8AKwBIAVoBsAAmAJYAAAAHAJYAjQAA%2F%2F8ANgBIAWUBsAAmAJcAAAAHAJcAjQAAAAEAKADmAQQBDQADAA0AuwABAAEAAgAEKzAxEzMVIyjc3AENJwAAAP%2F%2FACgA5gEEAQ0CBgCaAAAAAQAoAOgBuAEMAAMADQC7AAEAAQACAAQrMDETIRUhKAGQ%2FnABDCQAAAEAKADoAvgBDAADAA0AuwABAAEAAgAEKzAxEyEVISgC0P0wAQwkAP%2F%2FAEMBFQCYAXACBwCGAAABIQAAAAEAKACaAPEBdgATAAsAugAKAAAAAyswMTciLgI1ND4CMzIeAhUUDgKMEyQcEREcJBMTJRwRERwlmg8dKBoZKRwQEBwpGRooHQ8AAAABAAz%2FiwHo%2F7EAAwANALsAAAABAAEABCswMQUVITUB6P4kTyYmAAAAAQBY%2F1EA9wLbAA4ACwC6AAYAAAADKzAxFy4BNTQ2NxcOARUUFhcH3D5GRj4bPDw8PBuvZN2EhN1kEF%2Ffd3ffXxAAAAABACD%2FUQC%2FAtsADgALALoABwANAAMrMDEXPgE1NCYnNx4BFRQGBycgPDw8PBs9R0c9G59f33d3318QZN2EhN1kEAAAAAEAYv9oAQICxAAHABcAuwAFAAEABgAEK7sAAQABAAIABCswMRMzFSMRMxUjYqB9faACxB383h0AAQAV%2F2gAtQLEAAcAFwC7AAAAAQAFAAQruwAEAAEAAQAEKzAxFxEjNTMRIzWSfaCgewMiHfykHQABACP%2FaAECAsQAMwArALsAAAABAAEABCu7AB4AAQAfAAQruwAQAAEADwAEK7oAKgAPABAREjkwMQUVIyIuAjU0NjU0LgIjNTI%2BAjU0JjU0PgI7ARUjIgYVFBYVFAYHFR4BFRQGFRQWMwECIhwpHA4IBxMiGhoiEwcIDhwpHCIfLh8GFR4eFQYfLnsdDB0xJjhhNA8eFg4gDhYcDzZhOCYxHQwdMjUxWTYtMgkECTQrNlkxNTIAAAAAAQAV%2F2gA9ALEADMAKwC7AAAAAQAxAAQruwAWAAEAEwAEK7sAIwABACQABCu6AAoAJAAjERI5MDEXMjY1NCY1NDY3NS4BNTQ2NTQmKwE1MzIeAhUUBhUUHgIzFSIOAhUUFhUUDgIrATU0Lh8GFh0dFgYfLh8iHCkcDggHEyIaGiITBwgOHCkcInsyNTFZNis0CQQJMi02WTE1Mh0MHTEmOGE2DxwWDiAOFh4PNGE4JjEdDB0AAQAJ%2F2ABYALGAAMAGAC4AABFWLgAAC8buQAAABI%2BWbgAAtwwMQEzASMBOib%2BzyYCxvyaAAEAX%2F8GAIMC7gADAAsAugABAAIAAyswMRMzESNfJCQC7vwYAAEABf9gAV0CxgADABgAuAAARVi4AAAvG7kAAAASPlm4AALcMDETMwEjBSYBMiYCxvyaAAACAF%2F%2FBgCDAu4AAwAHAAsAugABAAUAAyswMRMzESMXESMRXyQkJCQC7v4pO%2F4qAdYAAAAAAQBGAc4BRQLIAA4AFAC4AABFWLgABS8buQAFABI%2BWTAxEzcnNxc3Mxc3FwcXBycHaTdaCV8HIAdfClo2G0BCAeJYJB4aZmUZHiRYFFNTAAACADL%2FzAGtAqkADwBJABcAuwAxAAEAKgAEK7sARgABABMABCswMSU%2BATU0LgInDgEVFB4CEy4BIyIOAhUUHgQVFAYHHgEVFA4CIyImJzceATMyNjU0LgQ1NDY3LgE1ND4CMzIWFwE4JCctQ0wfIiotQ0xNGDUmGSQWCyo%2BST4qMSYQEhcnNB00UB0eGjsuLjYqP0k%2FKjMmDxIQIjIjKkYctREqKSgzJB8UFC4mJzEjHgGLFBoNFRwOISkhHSk8LjI5FRApGxsuIRIjHBwYHjIjIisgHSg6LjA%2FFA8oGhUqIRUeFwACACr%2FsAGjApMAAwAQACUAuAAARVi4AAAvG7kAAAAQPlm4AABFWLgADi8buQAOABA%2BWTAxATMRIwMiLgI1ND4COwERAXUuLlI3XEIkIj5WMywCk%2F0dAUQXMU84OE8yF%2F5hAAAAAwAz%2F%2FQCsgKMABMAJwBFADMAuwA6AAEAQQAEK7sALQABADQABCu4AC0QuAAj3LkABQAB9LgAQRC4ABncuQAPAAH0MDETND4CMzIeAhUUDgIjIi4CNxQeAjMyPgI1NC4CIyIOAhc0PgIzMhYXBy4BIyIGFRQWMzI2NxcOASMiLgIzM1h0QUB0WDMzWHRAQXRYMyMuTmc6OWdOLi5OZzk6Z04ucx4xQSQoOBgXFy0ePExJPSQ4FxQbPjAkPzAcAUJLelYvL1Z6S0x7VzAwV3tMRHBPLCxPcERDb08rK09vQytGMBofGBoWFlJES1YdFRwYIxszSQAAAAAEABIBRAF%2BAsgAEwAnADcAQABXALgANi%2B4ACkvuAA2ELgAFNy5AAAAAfS4ACkQuAAe3LkACgAB9LoANAA2ACkREjm4ADQvuQA4AAH0ugAxADgANBESObgANhC4ADPQuAApELkAPgAB9DAxEyIuAjU0PgIzMh4CFRQOAicyPgI1NC4CIyIOAhUUHgIDMzIeAhUUBgcXIycjFSM3MjY1NCYrARXIJUIyHR0yQiUlQzEdHTFDJR84KBgYKDgfIDcoGBgoNyRFDxsWDRcRMSUnLyA9FxwVGyABRBwyRywsSDMcHDNILCxHMhwdGSs8JCQ9LRkZLT0kJDwrGQEPBg8YExMgBVZMTGcRFBEVSwAAAgA0%2F24C%2BwJ5AEYAVAA%2FALsAPAABAEIABCu7ACgAAQAPAAQruwBKAAEAFgAEK7sAIAABAFAABCu7AAUAAQAyAAQrugAjAFAAIBESOTAxNzQ%2BAjMyHgIVFA4CIyImJyMOASMiLgI1ND4CMzIXMzczBwYzMj4CNTQuAiMiDgIVFB4CMzI2NxcGIyIuAjcUFjMyPwEuASMiDgI0QW2QT0h1USwiNkEfKDUEAho9IxcqHxIZMEUsNR4CCSIkIl4YMykbJ0hnQUWAYzwsT29DMlIkEFZlSHtaM%2BguIjI6HxIhFyI2JRTKYp9xPS5TdUdAYUIhJScdKREiMyIkUEIrMCi9hx45UjVAakopOGeRWUl1USwaFh42L1mBUjkuQ60cFSM2QgAAAAIAJAAAAcECigAbAB8AmwC4AABFWLgACC8buQAIAA4%2BWbgAAEVYuAAMLxu5AAwADj5ZuAAARVi4ABYvG7kAFgAEPlm4AABFWLgAGi8buQAaAAQ%2BWbsAAwABAAAABCu7AAcAAQAEAAQruAAHELgACtC4AAcQuAAO0LgABBC4ABDQuAADELgAEtC4AAAQuAAU0LgAABC4ABjQuAADELgAHNC4AAQQuAAd0DAxNyM1MzcjNTM3MwczNzMHMxUjBzMVIwcjNyMHIz8BIwd3U1cVWFwZIxmVGiIZUlUVVlsZIxmVGiPXFZYV1iSqJMLCwsIkqiTW1tb6qqr%2F%2FwBeAbgA1gM%2BAgcAtwAAAbgAAP%2F%2FAC8BuAE3A0oCBwC4AAABuAAA%2F%2F8AKAGsATQDSgIHALkAAAG4AAD%2F%2FwAuAbgBPQM%2BAgcAugAAAbgAAAACACj%2F9AFCAZIACwAXACgAuAAGL7gAAEVYuAAALxu5AAAABD5ZuQAMAAH0uAAGELkAEgAB9DAxFyImNTQ2MzIWFRQGJzI2NTQmIyIGFRQWtUJLS0JCS0tCLzc3Ly83NwxrZWRqamRlayFbVFRZWVRUWwAAAAEAXgAAANYBhgAIACIAuAAGL7gAAEVYuAAILxu5AAgABD5ZuAAGELkAAAAB9DAxEyM1PgE3MxEjsFIdKREhJgFEGwYTDv56AAEALwAAATcBkgAaACwAuAAPL7gAAEVYuAAZLxu5ABkABD5ZuQAXAAH0uAAA0LgADxC5AAgAAfQwMTc%2BAzU0JiMiBgcnPgEzMhYVFA4CBzMVIzgySS8XLycbLxEZEkElNkMZLD8lwP8ZLkg6MRkrMiQaFx4rPT4fODo%2FJSIAAAAAAQAo%2F%2FQBNAGSACoAPgC4ABcvuAAARVi4ACcvG7kAJwAEPlm6AAoACQADK7gAJxC5AAMAAfS4ABcQuQAQAAH0ugAdAAkAChESOTAxNx4BMzI2NTQmIzUyNjU0JiMiBgcnPgEzMhYVFAYHHgMVFA4CIyImJ0USOSIlNkpAOz8tJhcvExkVOyYxRSsgEiAaDxQkMBsuRxRVHSMrKCYqHDIiICkdFxYbJDYxJTENAxAYIRUbKx0QLR4AAAIALgAAAT0BhgAFABAATAC4AA4vuAAARVi4AAkvG7kACQAEPlm7AAAAAQAKAAQruAAOELkAAgAB9LgAABC4AAXQuAAKELgAB9C4AAUQuAAM0LgAABC4AA%2FQMDE3NTcjDwEXIxUjNSM1NzMVM98EBDhJ3zoksbEkOpJXaFNsIHJyFf%2F0%2F%2F8AKgEFAR0CUwIGAL0AAP%2F%2FACEBBQFFAlMCBgC%2BAAAAAgAqAQUBHQJTABsAJAA7ALgAAC%2B4ABIvuwAGAAEAHwAEK7gAEhC5AAsAAfS4AAAQuAAX0LgAABC5ABwAAfS6ABkAAAAcERI5MDETIiY1NDY3NC4CIyIGByc%2BATMyFh0BIycjDgEnMjc1DgEVFBaLKzZibAcQGhUePBEPFEMnOi0eBgQVNRcvNV1KJAEFMCs1NwoTIhkOGQwbDh1FOcgnEh0gMWYKLSIgHgAAAAIAIQEFAUUCUwATAB8AGwC4AAovuAAAL7kAFAAB9LgAChC5ABoAAfQwMRMiLgI1ND4CMzIeAhUUDgInMjY1NCYjIgYVFBazHjYnFxcnNh4eNicXFyc2HjA7OzAwOzsBBRcrPicoPisWFis%2BKCc%2BKxchSjw8Sko8PEoAAAACACoBvwEOAqwAEwAfABcAuwAUAAEAAAAEK7sACgABABoABCswMRMiLgI1ND4CMzIeAhUUDgInMjY1NCYjIgYVFBacFikgExMgKRYWKSATEyApFiMtLSMjLS0BvxAeLBwcLB8QEB8sHBwsHhAgMSUmMjImJTEAAAACAB4AcgHAAiIAIgA2AEoAuAAARVi4AAgvG7kACAAKPlm4AABFWLgADS8buQANAAo%2BWbgAAEVYuAASLxu5ABIACj5ZuwAoAAEAHwAEK7gADRC5ADIAAfQwMT8BLgE1NDY3JzcXPgEzMhYXNxcHHgEVFAYHFwcnDgEjIicHNxQeAjMyPgI1NC4CIyIOAh5CEhQUEkIbQhc9ICA9F0MaQhIUFBFBGkMXPSBCMkItFiUyHBwyJRYWJTIcHDIlFo5DGDwjJD4YRBxFFRcXFUUcRBg%2BJCM8GEMcRBYXLUTWIjkoFxcoOSIiOikXFyk6AAEAO%2F%2BSAaMC7QAxAF0AuAAARVi4ACsvG7kAKwAOPlm4AABFWLgAFS8buQAVAAQ%2BWbgAKxC5AAUAAfS6AAgAFQArERI5uAAVELgAEtC4ABUQuQAcAAH0ugAfACsAFRESObgAKxC4AC7QMDEBLgMjIgYVFB4EFRQGBxUjNS4BJzceATMyNjU0LgQ1ND4CNzUzFR4BFwF4DhseJBc0QCtAS0ArV0MnNFYdGB1QND9DK0BLQCsVJTQfJzE%2BGgIoDhYQCEEyLDUnIS9HOUhWBmNjBC0cHxosRDcxPSsiLD8zHzYpGgNjYwMoGwABADYAAAGsAosALABZALgAAEVYuAATLxu5ABMADj5ZuAAARVi4AAIvG7kAAgAEPlm5AAAAAfS6ACQAEwACERI5uAAkL7gACtC4ACQQuQAjAAH0uAAL0LgACy%2B4ABMQuQAaAAH0MDElFSE1PgE1NCYnIzU3My4BNTQ2MzIWFwcuASMiDgIVFBYXMxUjHgEVFAYHFQGs%2Fo04MgUEZEIYCxVbTzRFFx0VNigfLx8QFQulnQQEJiIoKBsgaDoTJBEhAyZLJ09bKh0bGSIUJDAbKEknJBEjFD5SIQQAAAAAAQAeAAABwQJ%2FAB0AhAC4AABFWLgAHS8buQAdAA4%2BWbgAAEVYuAAJLxu5AAkADj5ZuAAARVi4ABQvG7kAFAAEPlm6AAQAHQAUERI5ugAZAB0AFBESObgAGS%2B5AAwAAfS4ABkQuAAN0LgAGRC4ABjQuAAYL7gAENC4ABgQuQAVAAH0uAAR0LgAGRC5ABsAAfQwMRMXHgEXMz4BPwEzAzMVIxUzFSMVIzUjNTM1IzUzA05eECASBBMfEV4urpqoqKgtpqammKwCf8EhQyUlQyHB%2FrAhRyKlpSJHIQFQAAEAGP%2F0AdoCiwA1AG0AuAAARVi4ABkvG7kAGQAOPlm4AABFWLgAAy8buQADAAQ%2BWbsALQABAC4ABCu4AC4QuAAJ0LgALRC4AArQuAAtELgAJdy4ABLQuAAlELkAJAAB9LgAE9C4ABkQuQAgAAH0uAADELkAMgAB9DAxJQ4BIyIuAicjNTcmNDU8ATcjNTc%2BAzMyFhcHLgEjIgYHIRUhBhQVHAEXMxUjHgEzMjY3AdogTjgsSjgmB0E%2BAQE%2BQQcmPFEwLUoWHRY3I1BgCwEW%2FucBAfHuDVpIKj8dUiwyJENfPB0ECxQLCRIIHQQ9YUQkLiAbHiV3aSEIEQkLFQshZnYoKQAAAgA%2B%2F%2BQBswKMAAgAJwA3ALsAJAABAAwABCu7ABwAAQAjAAQruAAjELgAANC4ACQQuAAI0LgADBC4AA%2FQuAAcELgAGdAwMRMOAxUUFhc3DgEHFSM1LgM1ND4CNzUzFR4BFwcuAScRPgE3%2FyA3JxZPRbQdSSwiKkczHR41RigiMEAXGBc2IiU%2BGAH2BSAxQidOZgoXGyMCbG0EIjtSMzRROyIEb20CIxccFBsC%2Fn8CHhYAAAAAAf9b%2F%2FQA8QKfAAMAGAC4AABFWLgAAC8buQAAAAQ%2BWbgAAdwwMQcBMwGlAXAm%2FpEMAqv9VQAAAP%2F%2F%2F1v%2F9ADxAp8CBgDGAAD%2F%2FwAo%2F%2FQC%2FQKfACcAtgAAAQ0AJwDGAWsAAAAHALYBuwAAAAD%2F%2FwBK%2F%2FQCzwKfACcAt%2F%2FsAQ0AJwDGAVYAAAAHALoBkgAAAAD%2F%2FwBK%2F%2FQC3wKfACcAt%2F%2FsAQ0AJwDGAT8AAAAHALgBqAAAAAD%2F%2FwAo%2F%2FQC4gKfACcAuQAAAQ0AJwDGAYUAAAAHALoBpQAAAAAAAQAiAG4BvQImAAsAHQC7AAMAAQAAAAQruAADELgABtC4AAAQuAAI0DAxEyM1MzUzFTMVIxUj27m5Kbm5KQE3JsnJJskAAAAAAQAiATcBvQFdAAMADQC7AAEAAQACAAQrMDETIRUhIgGb%2FmUBXSYAAAEAMwCHAasCDQALACUAuAAARVi4AAMvG7kAAwAKPlm4AABFWLgABS8buQAFAAo%2BWTAxPwEnNxc3FwcXBycHM6KiG6GiGqGhGqKho6enHKmpHKenHKioAAADACIAbQG9AiUACwAXABsAIQC7AA8AAQAVAAQruwAGAAEAAAAEK7sAGQABABoABCswMRMiJjU0NjMyFhUUBgM0NjMyFhUUBiMiJichFSHvEBYWEBEVFTcWEBEVFREQFqcBm%2F5lAdUWExEWFhETFv7BERYWERMWFtomAAD%2F%2FwAiANIBvQHDAiYAzQBmAAYAzQCbAAAAAQAiAJIBvQIGAAkAFAC4AABFWLgAAS8buQABAAo%2BWTAxEyUVDwEVHwEVJSIBm%2BiFhej%2BZQFipCtaMwQzWiukAAEAIgCSAb0CBgAJABQAuAAARVi4AAgvG7kACAAKPlkwMQEFNT8BNS8BNQUBvf5l6IWF6AGbATakK1ozBDNaK6QAAAAAAgAiAAABvQImAAsADwA4ALgAAEVYuAAOLxu5AA4ABD5ZuwADAAEAAAAEK7gAAxC4AAbQuAAAELgACNC4AA4QuQAMAAH0MDETIzUzNTMVMxUjFSMHIRUh27m5Kbm5KbkBm%2F5lATYly8slyUglAAAAAAEAQgEiAZ0CngAJABoAuAAARVi4AAAvG7kAAAAQPlm5AAUAAfQwMRMzEyMvASMPASPYLpcrTTQEM00rAp7%2BhMuFhcsAAQAoAQoBtgGKABcAJwC7AAgAAQAPAAQruAAPELgAFNy5AAMAAfS4AAvQuAAPELgAF9AwMRM%2BATMyHgIzMjY3Fw4BIyIuAiMiBgcoFjsdHS8rKBYXJhIcFTsdHS8rKBYXJhIBNyopHCIcHCMUKSgcIhwcIwABACIAbgG9AV0ABQANALsAAQABAAQABCswMRMhFSM1ISIBmyn%2BjgFd78kAAAEAXP84Ab0B4AAXABoAuAAARVi4ABEvG7kAEQAEPlm5AAUAAfQwMRMzERQWMzI2NxEzESMnIw4BIyImJxcVI1wsNDorRSssJgQDIkswJTQUAiwB4P7STUkvMwFi%2FiBQKjEWIVqaAAD%2F%2FwCzAjwBQwLEAAcA4AELAAAAAP%2F%2FANMCPAFjAsQABwDiAQsAAAAA%2F%2F8AmQI8AX0CxAAHAOQBCwAAAAD%2F%2FwCPAkUBhwKeAAcA5gELAAAAAP%2F%2FAJgCVgF%2BAp8ABwDpAQsAAAAA%2F%2F8AmgJeAXwCggAHAOgBCwAAAAD%2F%2FwC5AhsBXQLIAAcA6wELAAAAAP%2F%2FAMr%2FMAFOAAIABwDtARQAAAAAAAH%2FqAI8ADgCxAADABgAuAAARVi4AAMvG7kAAwAMPlm4AAHcMDEDMxcjWDRcJgLEiAAAAAAB%2F48CxQAlAy8AAwALALoAAQADAAMrMDEDMxcjcTpcKAMvagAB%2F8gCPABYAsQAAwAYALgAAEVYuAAALxu5AAAADD5ZuAAC3DAxAyM3MxImXDQCPIgAAAAAAf%2FbAsUAcQMvAAMACwC6AAIAAAADKzAxEyM3MwMoXDoCxWoAAf%2BOAjwAcgLEAAcANwC4AABFWLgABi8buQAGAAw%2BWbgAAEVYuAADLxu5AAMADD5ZuAAGELgAANy6AAUAAAAGERI5MDEDMxcjJyMHIxMmXyVLBEslAsSIY2MAAAAB%2F4sCxQB1Ay8ABwAXALoABgABAAMruAAGELgAB9y4AAPQMDEDNzMXIycjB3VgKmAoSwRLAsVqakhIAAAB%2F4QCRQB8Ap4AGwBBALgAAEVYuAAALxu5AAAADD5ZuAAARVi4ABMvG7kAEwAMPlm7ABgAAQAFAAQruAATELkACgAB9LgABRC4AA7QMDEDPgMzMh4CMzI2NzMOAyMiLgIjIgcjfAEIERkSEh4aGA0QEgQeAQgRGRISHhkZDR4IHgJFECAZEBEVER0aECAZEBEVETcAAf9%2BAswAggMoABsAIwC6AAAAEwADK7oABQAOAAMruAAAELgACNC4AA4QuAAW0DAxEyIuAiMiBgcjPgMzMh4CMzI2NzMOAzsUHxsaDxAWAx0BChIZERQeGxsPEBYDHQEKEhkCzBIVEh8aESEaEBIVER4aESAbEAAAAf%2BPAl4AcQKCAAMACwC4AAMvuAAB3DAxAzMVI3Hi4gKCJAAAAv%2BNAlYAcwKfAAsAFwAoALgAAEVYuAAALxu5AAAADD5ZuAAG3LgAABC4AAzQuAAGELgAEtAwMQMiJjU0NjMyFhUUBjMiJjU0NjMyFhUUBk8QFBQQEBUVjhAVFRAQFBQCVhUQDxUVDxAVFRAPFRUPEBUAAAAC%2F40C1QBzAx4ACwAXABsAugAGAAAAAyu4AAAQuAAM0LgABhC4ABLQMDEDIiY1NDYzMhYVFAYzIiY1NDYzMhYVFAZPEBQUEBAVFY4QFRUQEBQUAtUTEREUFBERExMRERQUERETAAAAAAL%2FrgIbAFICyAALABcAEwC6AAwAAAADK7oABgASAAMrMDERIiY1NDYzMhYVFAYnMjY1NCYjIgYVFBYjLy8jIy8vIxYeHhYWHh4CGzAmJzAwJyYwGiEbHCEhHBshAAAAAAL%2FrgK8AFIDYAALABcAEwC6AAwAAAADK7oABgASAAMrMDERIiY1NDYzMhYVFAYnMjY1NCYjIgYVFBYjLy8jIjAwIhQfHxQWHh4CvCwmJS0tJSYsGhwcGh4eGhwcAAAAAAH%2Ftv8wADoAAgARABMAugACABEAAyu6AAsACgADKzAxJzMHHgEVFA4CByc%2BATU0JicIIx0YJBUjLRgHKjMmHQI4CB8eFB0UDQMeBhgVFxYIAAAAAAH%2Ftv8wADoAAgARABMAugARAAIAAyu6AAoACwADKzAxJzMHHgEVFA4CByc%2BATU0JicIIx0YJBUjLRgHKjMmHQI4CB8eFB0UDQMeBhgVFxYIAAAA%2F%2F8ASwAAAJsCowIGACYAAAABAAAA8QBcAAcAeAAFAAEAAAAAAAoAAAIAAXMAAwABAAAAYgBiAGIAYgCwARQBZAGoAeYCHgJ%2BAroC2gMOA1YDfAPgBDIEhgTOBUAFlgYEBjAGdga0ByoHhgfGB%2FwIegj0CUQJvgogCnALIgtwC6QL7Aw0DGQM4g04DYwOBg5%2BDsgPNg%2BED9oQFhCMEOYROhFwEXwRiBGUEaARrBG4Eh4SKhI2EkISThJaEmYSchJ%2BEooSlhKiEq4SuhLGEtITZBPCE84T2hPmE%2FIT%2FhReFKAUrBS4FMQU0BTcFOgVqhW2FcIVzhXaFeYV8hX%2BFgoWFhY2FkIWThZaFmYWchZ%2BFuoXhBgEGBAYHBgoGDQYQBhMGNIZOBmyGjga7hsuG2gbtBwqHHwc3B1EHXoeEB54HpoexB7QHtwfEB88H44f4B%2F2IAIgKCBOIFogZiBwIH4gliCuILogxiDaIOIg9iEKIRQhOiFOIXAhkiGuIcoiJiKAIpoirCLGIuAjCCN6I6wkKCSuJUAlvCXGJdAl2iXkJh4mQiaCJt4nICcoJzAnhifEKAAodijqKVgpxipIKqIqvirGKtgq6i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M0FIER0oF14XMCYYHzdLLTtkIy0eSS43QxMgJhRdHDIkFR86UjRFdiuPJS07MBkjGRQLKQocKDckJUAvGi0kNh0hMy0YIRkTCCgMHyk3JCdEMx00LQAAAQAcAAAB%2FAKQAAcAMwC4AABFWLgAAi8buQACABA%2BWbgAAEVYuAAHLxu5AAcABD5ZuAACELkAAAAB9LgABdAwMRMjNSEVIxEj4sYB4MZUAkpGRv22AAAAAAEAV%2F%2F0Ai4CkAAZADwAuAAARVi4AAAvG7kAAAAQPlm4AABFWLgADS8buQANABA%2BWbgAAEVYuAAULxu5ABQABD5ZuQAHAAH0MDETMxEUHgIzMj4CNREzERQOAiMiLgI1V1MYKTggITgqGFAkP1YyMlc%2FJAKQ%2Fn07UDAVFTBQOwGD%2Fn9PbEMdHUNsTwAAAQAAAAACAwKQAA0AQAC4AABFWLgAAC8buQAAABA%2BWbgAAEVYuAAKLxu5AAoAED5ZuAAARVi4AA0vG7kADQAEPlm6AAUAAAANERI5MDERMxMeARczPgE3EzMDI1lpEhsTBBIcEWlV0GECkP6eO2Q6OmQ7AWL9cAAAAQAXAAAC%2BgKQACEAdgC4AABFWLgAAC8buQAAABA%2BWbgAAEVYuAAKLxu5AAoAED5ZuAAARVi4ABQvG7kAFAAQPlm4AABFWLgAIS8buQAhAAQ%2BWbgAAEVYuAAXLxu5ABcABD5ZugAFAAAAIRESOboADwAUABcREjm6ABwAIQAKERI5MDETMxMeARczPgE3EzMTHgEXMz4BNxMzAyMDLgEnIw4BBwMjF1ZFCRQJBAsYC1tMWwwYDAQJEgpFUIhkYwkPCAQIEQhhYwKQ%2Fps2aDY2aTUBZf6bNGo2Nmk1AWX9cAGLJkkmJkkm%2FnUAAAABAA8AAAHyApAAGQBbALgAAEVYuAABLxu5AAEAED5ZuAAARVi4AAsvG7kACwAQPlm4AABFWLgAGS8buQAZAAQ%2BWbgAAEVYuAAPLxu5AA8ABD5ZugAGAAEAGRESOboAEwAPAAsREjkwMRMDMxceARczPgE%2FATMDEyMnLgEnIw4BDwEjzrJcWQ0XDwQOFQxXWLO%2FXGANGxAEDhoMX1gBUwE9qBcrHR0rF6j%2Bv%2F6xsRgzHh4zGLEAAAAAAf%2F%2FAAAB3QKQAA8AQAC4AABFWLgAAS8buQABABA%2BWbgAAEVYuAALLxu5AAsAED5ZuAAARVi4AA8vG7kADwAEPlm6AAYAAQAPERI5MDE3AzMXHgEXMz4BPwEzAxUjxMVZVRAeEQQRIg9UV8VU%2FgGSuSRGJSVGJLn%2Bbv4AAAEALQAAAfECkAAJAD0AuAAARVi4AAMvG7kAAwAQPlm4AABFWLgACC8buQAIAAQ%2BWbkABgAB9LgAANC4AAMQuQABAAH0uAAF0DAxNwEhNSEVASEVIS0BWf7GAaL%2BpgFd%2FjwyAhhGMf3oRwAAAAIAOv%2F0AbcB8gAbACcAdgC4AABFWLgADy8buQAPAAg%2BWbgAAEVYuAAZLxu5ABkABD5ZuAAARVi4ABQvG7kAFAAEPlm6AAMADwAZERI5uAADL7gADxC5AAgAAfS6ABUAFAAPERI5uAAZELkAHwAB9LgAFRC5ACIAAfS4AAMQuQAjAAH0MDE3NDY3NC4CIyIGByc%2BATMyFhURIycjDgEjIiY3FBYzMjY3NQ4DOo%2BcCRcmHitJHSEiYjtZUEQHAiNRLT5RUTEkIz8jPVQzFn5QVREXLCIVIBQ5FiltW%2F7WOh0pSEgqJCEghwgWHicAAgBS%2F%2FQB%2BwLIABYAJgCDALgAAEVYuAAALxu5AAAAEj5ZuAAARVi4AAYvG7kABgAIPlm4AABFWLgAEC8buQAQAAQ%2BWbgAAEVYuAAWLxu5ABYABD5ZugADAAYAEBESOboAEwAQAAYREjm4ABMQuQAXAAH0uAAQELkAGgAB9LgABhC5ACQAAfS4AAMQuQAmAAH0MDETMxUHPgEzMh4CFRQOAiMiJicjByM3HgEzMj4CNTQuAiMiB1JSAiFOKS9IMRkiOkwqIkkgAwdCUiA%2FGB4zJRUOHzEiO0cCyMJYHScjQVs4PmJEIx8dMGwcFxsxSC0oQi8aQgAAAAABAC7%2F9AGwAfIAIQA5ALgAAEVYuAAFLxu5AAUACD5ZuAAARVi4AB0vG7kAHQAEPlm4AAUQuQAMAAH0uAAdELkAFgAB9DAxNzQ%2BAjMyFhcHLgEjIg4CFRQeAjMyNjcXDgEjIi4CLiZAVS8wRRkpFi8dITgoFxYnOCEjORYlIVEsMFQ9I%2FI9X0IiIxc1ExgbMkUqKkQxGx0UNh0iIkFfAAAAAgAv%2F%2FQB2QLIABQAIwCDALgAAEVYuAAFLxu5AAUACD5ZuAAARVi4AAovG7kACgASPlm4AABFWLgAEi8buQASAAQ%2BWbgAAEVYuAANLxu5AA0ABD5ZugAIABIABRESOboADgAFABIREjm4ABIQuQAYAAH0uAAOELkAGwAB9LgACBC5ABwAAfS4AAUQuQAfAAH0MDE3ND4CMzIWFyc1MxEjJyMOASMiJjcUFjMyNjc1LgEjIg4CLyM6TCoqPiAEU0QHAx1LK1xtVUZAIjweHzkeHTMmFvI7X0IkHhpTu%2F04ORwphHtYYiEi%2FhwXGzFEAAAAAgAu%2F%2FQBygHyABsAJABRALgAAEVYuAAFLxu5AAUACD5ZuAAARVi4ABcvG7kAFwAEPlm6AAwABQAXERI5uAAML7gAFxC5ABAAAfS4AAUQuQAfAAH0uAAMELkAJAAB9DAxNzQ%2BAjMyHgIVFAchHgEzMjY3Fw4BIyIuAiU0JiMiDgIHLiU9TiouSTEaA%2F64BVdGIzsbHSBOMjFVPyQBVD85Gi8mGQTyPF9CIyA8VDQbEk9cFRE2FB4jQV5hS08VJzklAAAAAAEAHgAAAT8C1AAVAFYAuAAARVi4AAUvG7kABQAIPlm4AABFWLgAEi8buQASABI%2BWbgAAEVYuAAJLxu5AAkABD5ZuAASELkAAgAB9LgABRC5AAgAAfS4AAvQuAAFELgADtAwMQEmIyIdATMVIxEjESM1NzU0NjMyFhcBLRscRGdnUkJCRUkXKREChQxeTUP%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%2BWboAAwAGABQREjm4AAvQuAAGELkADwAB9LgAAxC5ABIAAfQwMRMzFQc%2BATMyFhURIxE0JiMiBgcRI1JSAyNMM01HUiwwJjolUgLIwmQhL2Be%2FswBKUU9JiX%2BoAACAEMAAAC1ArQACwAPAC0AuAAARVi4AAwvG7kADAAIPlm4AABFWLgADi8buQAOAAQ%2BWboABgAAAAMrMDETIiY1NDYzMhYVFAYHMxEjfBghIRgYISFCUlICSh4XGB0dGBceZP4aAAAAAAL%2F2P8nALUCtAAPABsANwC4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAAFLxu5AAUABj5ZugAWABAAAyu4AAUQuQAMAAH0MDETMxEUBiMiJic3HgEzMjY1EyImNTQ2MzIWFRQGU1I8SRckDREJGA0kGCoYISEYFyEhAeb940pYCAU%2BAwUyLQKBHhcYHR0YFx4AAAEAUgAAAeYCyAAMAFsAuAAARVi4AAQvG7kABAAIPlm4AABFWLgAAC8buQAAABI%2BWbgAAEVYuAAMLxu5AAwABD5ZuAAARVi4AAgvG7kACAAEPlm6AAIAAAAMERI5ugAJAAAACBESOTAxEzMRMxMzBxMjJwcVI1JRA89bo7lajltRAsj%2BHgEAw%2F7d6mqAAAAAAAEAUv%2F0ANgCyAAPACsAuAAARVi4AAAvG7kAAAASPlm4AABFWLgADC8buQAMAAQ%2BWbkABQAB9DAxEzMRFBYzOgE3Fw4BIyImNVJSDgkEBwcLCBYRLygCyP2UFBACPgQEODYAAAABAFIAAALxAfIAIQCYALgAAEVYuAAGLxu5AAYACD5ZuAAARVi4AAAvG7kAAAAIPlm4AABFWLgAIS8buQAhAAQ%2BWbgAAEVYuAAZLxu5ABkABD5ZuAAARVi4ABEvG7kAEQAEPlm6AAIAAAAhERI5ugAJAAAAIRESObgABhC4AAzQuQAVAAH0uAAJELkAFwAB9LgABhC5AB0AAfS4AAIQuQAfAAH0MDETMxczPgEzMhYXPgEzMhYVESMRNCYjIgcRIxE0JiMiBxEjUkQHAyBLLDg%2FDyZNLUtJUiwuN0NSLC83Q1IB5kYjLzEsKjNgXv7MASlFPUv%2BoAEpRT1L%2FqAAAAAAAQBSAAAB1wHyABQAZQC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAALxu5AAAACD5ZuAAARVi4ABQvG7kAFAAEPlm4AABFWLgACy8buQALAAQ%2BWboAAgAAABQREjm4AAYQuQAPAAH0uAACELkAEgAB9DAxEzMXMz4BMzIWFREjETQmIyIGBxEjUkQHAyNNM01HUiwwJjolUgHmRiMvYF7%2BzAEpRT0mJf6gAAAAAAIALv%2F0AfAB8gATACcANQC4AABFWLgABS8buQAFAAg%2BWbgAAEVYuAAPLxu5AA8ABD5ZuQAZAAH0uAAFELkAIwAB9DAxNzQ%2BAjMyHgIVFA4CIyIuAjcUHgIzMj4CNTQuAiMiDgIuJT5RLS1RPiUlPlEtLVE%2BJVUUJTQfHzQlFBQlNB8fNCUU8j1fQiIiQl89PF9BIiJBXzwqRDEbGzFEKipFMhsbMkUAAAIAUv8zAfsB8gAWACcAgwC4AABFWLgACS8buQAJAAg%2BWbgAAEVYuAADLxu5AAMACD5ZuAAARVi4AAIvG7kAAgAGPlm4AABFWLgAEy8buQATAAQ%2BWboABQAJABMREjm6ABYAEwAJERI5uAAWELkAFwAB9LgAExC5ABoAAfS4AAkQuQAkAAH0uAAFELkAJwAB9DAxFxUjETMXMz4BMzIeAhUUDgIjIiYnNx4BMzI%2BAjU0LgIjIgYHpFJEBwMhTysvSDAZIjpMKiJDIgIhPhgeMyUVDh8xIh8%2FJCmkArM4HCgjQVs5PmFEIx4aQBwXGzFILShCLxoiIAAAAgAv%2FzMB2QHyABQAIwB%2FALgAAEVYuAAFLxu5AAUACD5ZuAAARVi4AAsvG7kACwAIPlm4AABFWLgADS8buQANAAY%2BWbgAAEVYuAASLxu5ABIABD5ZugAIAAUAEhESOboADwASAAUREjm5ABgAAfS4AA8QuQAbAAH0uAAIELkAHAAB9LgABRC5AB8AAfQwMTc0PgIzMhYXMzczESM1Nw4BIyImNxQWMzI2NzUuASMiDgIvIzpMKipAIQIIQlMEHUsqXG1VRkAiPB4fOR4dMyYW8jtfQiQdHS79Ta1WGyeEe1hiISL%2BHBcbMUQAAAABAFIAAAFeAfIAEQBSALgAAEVYuAAGLxu5AAYACD5ZuAAARVi4AAAvG7kAAAAIPlm4AABFWLgAES8buQARAAQ%2BWboAAgAAABEREjm4AAYQuAAM3LgAAhC5AA8AAfQwMRMzFzM%2BATMyFwcuASMiBgcRI1JEBwMZRyodFxAMFA8fQxlSAeZYLjYKSAQEMj7%2ByAABABz%2F9AGDAfIAMQBJALgAAEVYuAAVLxu5ABUACD5ZuAAARVi4AC4vG7kALgAEPlm5AAMAAfS6AAsALgAVERI5uAAVELkAHAAB9LoAJAAVAC4REjkwMTceATMyNjU0LgInLgM1ND4CMzIWFwcuASMiBhUUHgIXHgMVFA4CIyImJ0UgQywwMBQfKBQaNCkaFys%2BJy5NHCcZNiAuKxIeJxUaNSobFy1DKzReI24aICwgExwVEAgJFyEsHx0zJRUgFzQTGCocERkTDwgKFiEwIh40KBcmHQAAAAABABj%2F9AFFAm4AGQBFALgAAEVYuAAGLxu5AAYACD5ZuAAARVi4ABQvG7kAFAAEPlm4AAYQuQAJAAH0uAAA0LgABhC4AAPQuAAUELkADQAB9DAxEyM1PwEzFTMVIxEUFjMyNjcXDgEjIi4CNWBITApFg4MhKg0eDBAULxcnNSEOAaM%2BBYiIQ%2F7yLTEIBT4HCxgqPCQAAQBL%2F%2FQBzgHmABQAZQC4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAAJLxu5AAkACD5ZuAAARVi4ABEvG7kAEQAEPlm4AABFWLgADC8buQAMAAQ%2BWbgAERC5AAUAAfS6AA0ACQAMERI5uAANELkACAAB9DAxEzMRFBYzMjY3ETMRIycjDgEjIiY1S1MrMCY6I1JEBwMiSzNORwHm%2FtdFPScrAVn%2BGkwoMGBeAAAAAAEADAAAAccB5gANAEAAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgACi8buQAKAAg%2BWbgAAEVYuAANLxu5AA0ABD5ZugAFAAAADRESOTAxEzMTHgEXMz4BNxMzAyMMVVwLFwsECxYLXFGsYAHm%2FuwkSCMjSCQBFP4aAAEAGAAAArYB5gAhAHYAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgACi8buQAKAAg%2BWbgAAEVYuAAULxu5ABQACD5ZuAAARVi4ACEvG7kAIQAEPlm4AABFWLgAFy8buQAXAAQ%2BWboABQAhAAAREjm6AA8AFwAUERI5ugAcAAAAIBESOTAxEzMTHgEXMz4BNxMzEx4BFzM%2BATcTMwMjAy4BJyMOAQcDIxhUSAgOBwQIEAlLUEwJEQgECA4IR06CZEYJDwkECBAKRGAB5v7nI0IiIkMiARn%2B5yNCIiJCIwEZ%2FhoBBSNEJSVFI%2F78AAAAAQAOAAABsAHmABkAWwC4AABFWLgAAS8buQABAAg%2BWbgAAEVYuAALLxu5AAsACD5ZuAAARVi4ABkvG7kAGQAEPlm4AABFWLgADy8buQAPAAQ%2BWboABgALAA8REjm6ABQAAQAZERI5MDE3JzMXHgEXMz4BPwEzBxcjJy4BJyMOAQ8BI62TWUELGA0ECxYLO1aTnllHDRoOBA0YDEJW%2FuhrFCkUFCkUa%2FH1cRYsFRUrF3EAAAAAAQAM%2Fy8BxwHmAB0ARgC4AABFWLgACC8buQAIAAg%2BWbgAAEVYuAASLxu5ABIACD5ZuAAARVi4ABkvG7kAGQAGPlm5AAMAAfS6AA0ACAAZERI5MDEXHgEzMjY%2FAQMzEx4BFzM%2BATcTMwMOAyMiJic3MQgUCSo1DwvDVWMLGQsECxQKV1C3DSAsOCURHAwQhgIFOy0kAef%2B8yBHIiFIIAEN%2FfIkPi0aBQVBAAAAAAEAHwAAAY8B5gAJAD0AuAAARVi4AAMvG7kAAwAIPlm4AABFWLgACC8buQAIAAQ%2BWbkABgAB9LgAANC4AAMQuQABAAH0uAAF0DAxNwEjNSEVASEVIR8BAOQBTP8AAQj%2BkCwBd0Ms%2FolDAAAA%2F%2F8AAwAAAh0DMgImAAQAAAAHAOEBDwAA%2F%2F8AAwAAAh0DMgImAAQAAAAHAOMBDwAA%2F%2F8AAwAAAh0DMgImAAQAAAAHAOUBDwAA%2F%2F8AAwAAAh0DMwImAAQAAAAHAOcBDwAA%2F%2F8AAwAAAh0DLQImAAQAAAAHAOoBDwAA%2F%2F8AAwAAAh0DawImAAQAAAAHAOwBDwAAAAIACAAAAwUCkAAFABUAfAC4AABFWLgADS8buQANABA%2BWbgAAEVYuAAMLxu5AAwABD5ZuAAARVi4AAgvG7kACAAEPlm6AAIADQAMERI5ugAJAA0ADBESObgACS%2B5AAUAAfS4AAgQuQAGAAH0uAANELkADwAB9LoAFAANAAgREjm4ABQvuQASAAH0MDEBESMGDwEFFSE1IwcjASEVIRUzFSMVAZEENTY9AiD%2BjM5jWAFYAZv%2B6ujoAQIBTGtrdrtHv78CkEbOR%2B7%2F%2FwA0%2FysCGwKcAiYABgAAAAcA7gFXAAD%2F%2FwBaAAAB3gMyAiYACAAAAAcA4QEcAAD%2F%2FwBaAAAB3gMyAiYACAAAAAcA4wEcAAD%2F%2FwBaAAAB3gMyAiYACAAAAAcA5QEcAAD%2F%2FwBaAAAB3gMtAiYACAAAAAcA6gEcAAD%2F%2FwAAAAAAtgMyAiYADAAAAAcA4QCDAAD%2F%2FwBQAAABBgMyAiYADAAAAAcA4wCDAAD%2F%2F%2F%2F7AAABCwMyAiYADAAAAAcA5QCDAAD%2F%2F%2F%2F8AAABCgMtAiYADAAAAAcA6gCDAAD%2F%2FwBaAAACLQMzAiYAEQAAAAcA5wFGAAD%2F%2FwA0%2F%2FQCZQMyAiYAEgAAAAcA4QFMAAD%2F%2FwA0%2F%2FQCZQMyAiYAEgAAAAcA4wFMAAD%2F%2FwA0%2F%2FQCZQMyAiYAEgAAAAcA5QFMAAD%2F%2FwA0%2F%2FQCZQMzAiYAEgAAAAcA5wFMAAD%2F%2FwA0%2F%2FQCZQMtAiYAEgAAAAcA6gFMAAAAAwAy%2F%2BICawKuAAsAFQAwAIUAuAAARVi4ACwvG7kALAAQPlm4AABFWLgAHi8buQAeAAQ%2BWboAAAAeACwREjm5AAMAAfS6AAsALAAeERI5ugAMACwAHhESObgALBC5AA4AAfS6ABUAHgAsERI5ugAWACwAHhESOboAIQAeACwREjm6ACQAHgAsERI5ugAuACwAHhESOTAxNx4BMzI%2BAjU0Ji8BJiMiDgIVFBcBHgEVFA4CIyImJwcnNy4BNTQ%2BAjMyFzcX0BlAJixHMxwRECIzTCxIMxwiAXgfIilLZz00WSM%2FLkYgIilLZz5oRz8udBodJkdjPjBQIDI2JURiPWBEAYorc0hPf1kwISBTJFssdkhPfVcuP1EjAAIANAAAAx4CkAASABsAVQC4AABFWLgAAy8buQADABA%2BWbgAAEVYuAAOLxu5AA4ABD5ZuAADELkAGAAB9LgABdC6AAoAAwAOERI5uAAKL7kACAAB9LgADhC5ABcAAfS4AAzQMDETNDYzIRUhFTMVIxUhFSEiLgI3FBY7AREjIgY0qJkBn%2F7q6OgBIP5TS3VSK1Z6dzAwd3oBS52oRs5H7kctVXtOfokCCIQAAP%2F%2FAFf%2F9AIuAzICJgAYAAAABwDhAUIAAP%2F%2FAFf%2F9AIuAzICJgAYAAAABwDjAUIAAP%2F%2FAFf%2F9AIuAzICJgAYAAAABwDlAUIAAP%2F%2FAFf%2F9AIuAy0CJgAYAAAABwDqAUIAAP%2F%2F%2F%2F8AAAHdAzICJgAcAAAABwDjAO4AAAACACEAAAJKApAADAAbAFkAuAAARVi4ABsvG7kAGwAQPlm4AABFWLgAFi8buQAWAAQ%2BWbkAAAAB9LgAGxC5AAYAAfS6AAoAGwAWERI5uAAKL7kACQAB9LgAChC4ABjQuAAJELgAGdAwMSUyNjU0JisBFTMVIxUTMhYVFA4CKwERIzU3EQEOc3Nzc0uVlVGYnihOckqoT09Ein19hNwv%2FQJMqJ1Oe1UtAUErBAEgAAAAAAIAWgAAAhUCkAAQABkAOQC4AABFWLgAAC8buQAAABA%2BWbgAAEVYuAAQLxu5ABAABD5ZuwAZAAEADgAEK7sAAgABABgABCswMRMzFTMyHgIVFA4CKwEVIzcyNjU0JisBEVpTdjZaPyMjQFk2dlO%2FVlNUVWwCkG4ULkk2NE0yGJbaQEdHNv78AP%2F%2FADr%2F9AG3AsoCJgAeAAAABwDgAQ0AAP%2F%2FADr%2F9AG3AsoCJgAeAAAABwDiAQ0AAP%2F%2FADr%2F9AG3AsoCJgAeAAAABwDkAQ0AAP%2F%2FADr%2F9AG3Aq0CJgAeAAAABwDmAQ0AAP%2F%2FADr%2F9AG3Aq4CJgAeAAAABwDpAQ0AAP%2F%2FADr%2F9AG3AtcCJgAeAAAABwDrAQ0AAAADADr%2F9ALrAfIADgBAAEcAmwC4AABFWLgAEi8buQASAAg%2BWbgAAEVYuAAYLxu5ABgACD5ZuAAARVi4ADIvG7kAMgAEPlm4AABFWLgALC8buQAsAAQ%2BWbgAMhC5AAMAAfS6ADgAMgASERI5uAA4L7kACgAB9LoAHwAYACwREjm4AB8vuAAsELkAJQAB9LgAEhC5AD0AAfS4ABgQuQBEAAH0uAAfELkARwAB9DAxNxQWMzI2Ny4BJzUOAwM%2BATMyFhc%2BATMyHgIVFAchHgMzMjY3Fw4BIyImJw4BIyImNTQ2NzQuAiMiBgcFNCYjIgYHizEkIlAhCAoBOlEzFzwiYDY2Rg8dUTItRS8YA%2F7FARkoNh8jOBseIEwyPVIcMmUvPlGOmAgXJh4oSB0CMzs4M0kHhCokJyQTNRwZCBYeJwEYFik3MDA3IDxVNBwSJj4sGBcRORQeNyQtLkhCUFURFywiFSAUZEtQU0gAAAD%2F%2FwAu%2FysBsAHyAiYAIAAAAAcA7QEQAAD%2F%2FwAu%2F%2FQBygLKAiYAIgAAAAcA4AEJAAD%2F%2FwAu%2F%2FQBygLKAiYAIgAAAAcA4gEJAAD%2F%2FwAu%2F%2FQBygLKAiYAIgAAAAcA5AEJAAD%2F%2FwAu%2F%2FQBygKuAiYAIgAAAAcA6QEJAAD%2F%2FwAMAAAAvALKAiYAZwAAAAYA4HsAAAD%2F%2FwA6AAAA6gLKAiYAZwAAAAYA4nsAAAD%2F%2F%2F%2F6AAAA%2FALKAiYAZwAAAAYA5HsAAAD%2F%2F%2F%2F0AAABAgKuAiYAZwAAAAYA6XsAAAAAAQBSAAAApAHmAAMAJQC4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAACLxu5AAIABD5ZMDETMxEjUlJSAeb%2BGgAA%2F%2F8AUgAAAdcCrQImACsAAAAHAOYBJAAA%2F%2F8ALv%2F0AfACygImACwAAAAHAOABDwAA%2F%2F8ALv%2F0AfACygImACwAAAAHAOIBDwAA%2F%2F8ALv%2F0AfACygImACwAAAAHAOQBDwAA%2F%2F8ALv%2F0AfACrQImACwAAAAHAOYBDwAA%2F%2F8ALv%2F0AfACrgImACwAAAAHAOkBDwAAAAMALv%2FpAfAB%2FQAJABMALgBJALgAAEVYuAApLxu5ACkACD5ZuAAARVi4ABwvG7kAHAAEPlm6AAAAHAApERI5uQACAAH0ugAKACkAHBESObgAKRC5AAwAAfQwMTcWMzI%2BAjU0LwEmIyIOAhUUFwEeARUUDgIjIicHJzcuATU0PgIzMhYXNxeyJzYfNScVGBslOB81JhYXASQZHSU%2BUS1PPDElNhkdJT5RLSZIHTIkXScbMUQpQy8nKBsxRSlDLgEaIFc2PF9BIjE8HUEgVTY9X0IiGRk9HQAAAAMALv%2F0AyEB8gATADwAQwBxALgAAEVYuAAZLxu5ABkACD5ZuAAARVi4ADgvG7kAOAAEPlm5AAUAAfS4ABkQuQAPAAH0uAAZELgAH9C4ADgQuAAz0LoAJgAfADMREjm4ACYvuAAzELkALAAB9LgAHxC5AEAAAfS4ACYQuQBDAAH0MDE3FB4CMzI%2BAjU0LgIjIg4CBzQ%2BAjMyFhc%2BATMyHgIVFAchHgMzMjY3Fw4BIyImJwYjIi4CJTQmIyIGB4IUJDIeHjIkFBQkMh4eMiQUVCQ8UCw4XhocWTYtRjAZA%2F7BARkpNx8jOhseIE4yOV4cOXosTzwjAqs%2BODNKB%2FIqRDEbGzFEKipFMhsbMkUqPV9CIj48OUEgPFU0HBImPiwYFxE5FB5AOXkiQV9gS1BTSAABAFL%2F9AIjAtIANwBkALgAAEVYuAADLxu5AAMAEj5ZuAAARVi4ADcvG7kANwAEPlm4AABFWLgAGS8buQAZAAQ%2BWboADQAZAAMREjm5ACAAAfS6ACMAGQADERI5ugAvABkAAxESObgAAxC5ADIAAfQwMRM0NjMyHgIVFA4CFRQeBBUUDgIjIiYnNx4BMzI2NTQuBDU0PgI1NCYjIgYVESNSZl4nPSoVHCMcHSwzLB0WKjslKkQfIRozHSoqHSwzLB0bIRwpKjY7UgIDXnEXKDUeJjUsKRoYIBkaJDYoIDYoFxoXOhYVMCAdJhwZIC0jIjAsLiAmMU1O%2FgwAAP%2F%2FAEv%2F9AHOAsoCJgAyAAAABwDgARAAAP%2F%2FAEv%2F9AHOAsoCJgAyAAAABwDiARAAAP%2F%2FAEv%2F9AHOAsoCJgAyAAAABwDkARAAAP%2F%2FAEv%2F9AHOAq4CJgAyAAAABwDpARAAAP%2F%2FAAz%2FLwHHAsoCJgA2AAAABwDiAPIAAP%2F%2FAAz%2FLwHHAq4CJgA2AAAABwDpAPIAAAACADX%2F9AHlAtoAFAA4AFkAuAAARVi4ADMvG7kAMwASPlm4AABFWLgAHS8buQAdAAQ%2BWbsAJwABAAsABCu6ADAALQADK7gAHRC5AAAAAfS4AC0QuAAV0LgAMBC4ADPcuAAwELgANtAwMSUyPgI1NCYnLgEjIg4CFRQeAhMeARUUDgIjIi4CNTQ%2BAjMyFhcuAScHJzcmJzceARc3FwEPIjIiEQEBIUIiITQkExYnMms8TB85TzEqTjwkIDdLLCZGGg43Jo0YfzQ8JiRGII4YOB00SSwOHA0sHhgsOyImPSsYAiU9qHc8Y0cnID1XNjNTOyAgIj5cJkkpQSggNBQsG0kpAAAAAAIAUv8zAfsCyAAWACcAVwC4AABFWLgACC8buQAIAAg%2BWbgAAEVYuAACLxu5AAIAEj5ZuAAARVi4AAEvG7kAAQAGPlm4AABFWLgAEi8buQASAAQ%2BWbkAGgAB9LgACBC5ACQAAfQwMRcjETMVBz4BMzIeAhUUDgIjIiYnFzUeATMyPgI1NC4CIyIGB6RSUgEgTCgwSTIZIjpMKiNCIQEhPhgeMyUVDh8xIh8%2FJM0DlcJTGiUjQVs5PmFEIxwaU5UcFxsxSC0oQi8aIiAAAAEAHgAAAlsC1AAoAHwAuAAARVi4ABIvG7kAEgAIPlm4AABFWLgAJS8buQAlABI%2BWbgAAEVYuAAOLxu5AA4ABD5ZuAAlELkAAgAB9LgAEhC4ACHQuAAG0LgAEhC5AA8AAfS4AAvQuAAH0LgADhC4AArQuAASELgAEdC4ACUQuAAW0LkAHAAB9DAxASYjIh0BMxUjESMRIxEjESM1NzU0NjMyFhcHJiMiBh0BMzU0NjMyFhcCSh0aRWdnUspSQkJLTBgvEhEeIyQoykVJFykRAoUMXk1D%2Fl0Bo%2F5dAaM%2BBUBMWAoIPg0zMD5NS1YJBwAAAAEAHv%2F0AkUC1AArAIkAuAAARVi4ABAvG7kAEAAIPlm4AABFWLgAFC8buQAUABI%2BWbgAAEVYuAAMLxu5AAwABD5ZuAAARVi4AAMvG7kAAwAEPlm4ABAQuQANAAH0uAAJ0LgAEBC4AA%2FQuAAPL7gAFBC5ABoAAfS4ABAQuAAe0LgAItC4AAkQuAAj0LgAAxC5ACgAAfQwMSUOASMiLgI1ESMRIxEjNTc1NDYzMhYXByYjIh0BMzczFTMVIxEUFjMyNjcCRRQvFyc1IQ6uUkJCRUkXKRESGxxEsgpEhIQiKg0eDAYHCxgqPCQBDf5dAaM%2BBU1LVgkHPwxeTYiIQ%2F7yLTEIBQAAAwAg%2F%2FQCUgKcAA0AGwBJAIwAuAAARVi4ADQvG7kANAAQPlm4AABFWLgAIi8buQAiAAQ%2BWbgAAEVYuAAcLxu5ABwABD5ZugA%2FAB8AAyu4ACIQuQAFAAH0ugAIAB8APxESOboACwAiADQREjm4AAsvuAAR3LgANBC5ABkAAfS6ACwACwARERI5ugA8AAsAERESOboARgAfAD8REjkwMTcUHgIzMjY3LgEnDgETFBYXPgM1NCYjIgYBLgEnDgEjIi4CNTQ%2BAjcuATU0PgIzMhYVFA4CBx4BFz4BNzMOAQceARdwFCMvGyI%2BHTBZIyMvTREOFikfEh0hJSwBfyNMKCZdOi1JNR0VJC8ZFBcWKDgiPUQaKjUbIFcvHi8PTRQ4JyI%2BG68bLSARHBkqZDUcPQEtGzoeDx8hJRYdKzb9yQolHCIpGzBDKCE2LicRKU0kITgqGEg6IDYvKRQzXicpYDlBdjQXIAgAAAAAAgAs%2F%2FQBxQKKAAsAHQA1ALgAAEVYuAAGLxu5AAYADj5ZuAAARVi4AAAvG7kAAAAEPlm5AAwAAfS4AAYQuQAWAAH0MDEXIiY1NDYzMhYVFAYnMj4CNTQuAiMiDgIVFBb5YWxsYWBsbGAcLSESEiEtHBwuIRJFDKyhoaiooaGsQh9BZUZGZD8eHj9kRox%2FAAAAAQBPAAABtwJ%2BAAwAQwC4AABFWLgABy8buQAHAA4%2BWbgAAEVYuAAMLxu5AAwABD5ZuQABAAH0uAAHELkABAAB9LkAAgAB9LgAARC4AAnQMDE3MxEjNT4BNzMRMxUhT5J0LEEaP4T%2BmEQB1jUIFxD9xkQAAAABACQAAAHEAooAHQA9ALgAAEVYuAAPLxu5AA8ADj5ZuAAARVi4ABwvG7kAHAAEPlm5ABoAAfS4ABfQuAAA0LgADxC5AAgAAfQwMTc%2BAzU0JiMiBgcnPgEzMhYVFA4CBz4BOwEVIShIcEwoPD0oRBwvKFo%2FWWYnRV85GjgZuf5kMUh0Y1MnN0YtIC8sNWdVLVthaTsCBEcAAAEAGv%2F0Ab4CigAzAFMAuAAARVi4ABsvG7kAGwAOPlm4AABFWLgALi8buQAuAAQ%2BWbkAAwAB9LoACwAbAC4REjm4AAsvuQAMAAH0uAAbELkAFAAB9LoAJAAMAAsREjkwMTceATMyNjU0LgIjNTI%2BAjU0JiMiBgcnPgEzMh4CFRQGBxUeAxUUDgIjIi4CJ0QdTTk6ShUwTjkzRSsSOzMoQx0sJVk5KkYzHEA0HTImFSE5TCwmPzQpEIQeLj82HC8iEj8SICwZLzYkHTQjLRYpPCc6ShQEBxspNiEqRC8ZDxkgEgAAAAIAEQAAAdUCfgAJABQAVwC4AABFWLgAEi8buQASAA4%2BWbgAAEVYuAANLxu5AA0ABD5ZuwAOAAEAAAAEK7gAEhC4AATcuAAAELgACdC4AA4QuAAL0LgACRC4ABDQuAAAELgAE9AwMSU1NDY3Iw4BDwEFIxUjNSE1ATMRMwEwAwIEDBoOlQFtV07%2B4QERXFfyuRpHGhcsF9pCsLA2AZj%2BdAABABn%2F9AHBAn4AJgBHALgAAEVYuAAQLxu5ABAADj5ZuAAARVi4ACEvG7kAIQAEPlm5AAMAAfS6ABcAEAAhERI5uAAXL7gAC9y4ABAQuQASAAH0MDE3HgEzMj4CNTQmIyIGBycTIRUjBz4BMzIeAhUUDgIjIi4CJ0EcTTgdMyYWSj4hLx0sFQE%2F9xEXLh0pSDYfJDxNKiY%2FMykQgR0sFSY2IUJKFBMcATNHvQwOGDFLNDRQNx0PGB8RAAAAAAIAMP%2F0AckCigANAC4AQwC4AABFWLgAKy8buQArAA4%2BWbgAAEVYuAAhLxu5ACEABD5ZuwAZAAEACAAEK7gAIRC5AAAAAfS4ACsQuQARAAH0MDElMj4CNTQmIyIGBx4BEy4BIyIOAgc%2BATMyFhUUDgIjIi4CNTQ%2BAjMyFhcBDBgoHhE6PB5HIAhHwBQ3HiE8LhwBHlAnU2MeNEQnL1A7IihEVzA0Sxs1FCUzID9IJy1eYQHeFxscQGdMJStiYy5LNh4mTXNNYIdVJycdAAEALAAAAccCfgAPADMAuAAARVi4AAcvG7kABwAOPlm4AABFWLgAAC8buQAAAAQ%2BWbgABxC5AAUAAfS4AAnQMDEzPgM3ITUhFQ4DByOxBBgrQy%2F%2BwgGbOUcqEwRVWpaHfkJHM0iEiZldAAAAAAMAKf%2F0AcgCigAPAB0ARQBXALgAAEVYuAAsLxu5ACwADj5ZuAAARVi4AEEvG7kAQQAEPlm5AAUAAfS6AA0AQQAsERI5uAANL7gAENC4ACwQuQAWAAH0uAANELgAI9y4ABAQuAA23DAxNxQeAjMyNjU0LgInDgE3PgE1NCYjIgYVFB4CBzQ%2BAjc1LgE1ND4CMzIeAhUUDgIHFR4DFRQOAiMiLgJzFSUyHThFHDA%2FIiYztCAjOjUtOhgpNeEVISsXIzYcMEInKkIvGREZHw8VKB8THjZMLi1NNyCrGywhEj4yHywhGw4aRYUdQCMwQTgvHSkgGcQfNSshDAQZRzMlPCsYGS0%2FJRkuKCALBAwfJzIgJD4uGhovQAACACj%2F9AHAAooADQAuAEMAuAAARVi4ACEvG7kAIQAOPlm4AABFWLgAKy8buQArAAQ%2BWbsAAAABABkABCu4ACEQuQAGAAH0uAArELkAEQAB9DAxEzI2Ny4BIyIOAhUUFgceATMyPgI3DgEjIiY1ND4CMzIeAhUUDgIjIiYn6x9HIAhIPRcpHhE6ThQ3HiI8LhwBHlAoU2IeM0UmL1E7IShEVzAzTRoBNicuXmAUJTQfP0jLFxwcQWhNJixiYy5LNh4mTXNNYIdVJyYdAAABAEH%2F9AC4AHIACwAYALgAAEVYuAAJLxu5AAkABD5ZuAAD3DAxNzQ2MzIWFRQGIyImQSMZGCMjGBkjMh0jIx0bIyMAAAEAL%2F9WAMYAcgARABgAuAAARVi4AAUvG7kABQAEPlm4AAvcMDEXPgE1BiMiJjU0NjMyFhUUBgcvKjADBxgjJBkgJUY9ehM%2BKQEdHBsfNC1BYBoA%2F%2F8AQf%2F0ALgB2wInAIYAAAFpAAYAhgAA%2F%2F8AL%2F9WAMYB2wInAIYAAAFpAAYAhwAAAAIAVf%2F0AMwCngAFABEAGgC4AABFWLgADy8buQAPAAQ%2BWbkACQAB9DAxEyczBwMjBzQ2MzIWFRQGIyImaQJTAgs5HyMZGCMjGBkjAkBeXv6GlB0jIx0bIyMAAAAAAgBV%2F0gAzAHyAAUAEQAaALgAAEVYuAAPLxu5AA8ACD5ZuQAJAAH0MDEfASM3EzM3FAYjIiY1NDYzMha4AlMCCzkfIxgZIyMZGCNaXl4BepQdIyMdGyMjAAIAJv%2F0AXkCqgAbACcAKAC4AABFWLgAJS8buQAlAAQ%2BWbsAEQABAAoABCu4ACUQuQAfAAH0MDE3Jj4ENTQmIyIGByc%2BATMyFhUUDgQXBzQ2MzIWFRQGIyImoAYRHiciFzEwITsXLyBSNk5dGCMnIRIEXSIZGSMjGRkixic%2FNS4tLhsoOR8bKyQvVUshNjAvMjkjlB0jIx0bIyMAAAIAMP88AYMB8gAbACcAKAC4AABFWLgAJS8buQAlAAg%2BWbsACgABABEABCu4ACUQuQAfAAH0MDEBFg4EFRQWMzI2NxcOASMiJjU0PgQnNxQGIyImNTQ2MzIWAQkFEB4nIhcwMSE6FzAgUjZOXRgjJyASBF4jGBkjIxkYIwEgJz81Li0vGig4HhsrIzBVSyE2MC8yOSOUHSMjHRsjIwAAAAABAFABrwCoArIABQALALoAAgAEAAMrMDETJzMPASNTA1gDEDICVlxcpwAA%2F%2F8AUAGvAVgCsgAmAI4AAAAHAI4AsAAAAAEAOQGsALsCuAARAA0AuwAFAAEACwAEKzAxEw4BFTYzMhYVFAYjIiY1NDY3uyYmAwYUIR4XHiE2NAKRGTgrARoZGh0uLDxYHgAAAAABAD8BrwDBArsAEQANALsACwABAAUABCswMRM%2BATUGIyImNTQ2MzIWFRQGBz8mJQMFFSAeFx0iNzQB1Rk4LAEaGBoeLyw8Vx4AAAD%2F%2FwA5AawBawK4ACYAkAAAAAcAkACwAAD%2F%2FwA%2FAa8BcQK7ACYAkQAAAAcAkQCwAAD%2F%2FwA%2F%2F3AAwQB8AgcAkQAA%2FcEAAP%2F%2FAD%2F%2FcAFxAHwAJwCRAAD9wQAHAJEAsP3BAAAAAQAtAEIA2QG2AAYACwC6AAIABgADKzAxNzU3FwcXBy2IJHZ2JN0%2Bmx6cnhwAAAAAAQA2AEIA4gG2AAYACwC6AAIABQADKzAxNyc3FxUHJ6x2I4mJI%2FycHps%2BmxwAAAD%2F%2FwAtAEIBdwG2ACYAlgAAAAcAlgCeAAD%2F%2FwA2AEIBgAG2ACYAlwAAAAcAlwCeAAAAAQApANsBDwEaAAMADQC7AAEAAQACAAQrMDETMxUjKebmARo%2FAAAA%2F%2F8AKQDbAQ8BGgIGAJoAAAABACkA3wG3ARgAAwANALsAAQABAAIABCswMRMhFSEpAY7%2BcgEYOQAAAQApAN8C9wEYAAMADQC7AAEAAQACAAQrMDETIRUhKQLO%2FTIBGDkA%2F%2F8AQQEDALgBgQIHAIYAAAEPAAAAAQAoAI8BCAGAABMACwC6AAoAAAADKzAxNyIuAjU0PgIzMh4CFRQOApgXKB8SEh8oFxYpHxISHymPESAsGxstHxISHy0bGywgEQAAAAEADP%2BCAej%2FuQADAA0AuwAAAAEAAQAEKzAxBRUhNQHo%2FiRHNzcAAAABAFL%2FUAEJAtwADgALALoABgAAAAMrMDEXLgE1NDY3Fw4BFRQWFwfWPkZGPjM6OTk6M7Bk3oSE3WUYYNtzc9tgGAAAAAEAJv9QAN0C3AAOAAsAugAHAA0AAyswMRc%2BATU0Jic3HgEVFAYHJyY6OTk6Mz5GRj4zmGDbc3PbYBhl3YSE3mQYAAAAAQBe%2F2gBEQLEAAcAFwC7AAUAAQAGAAQruwABAAEAAgAEKzAxEzMVIxEzFSNes3V1swLEL%2F0CLwABAB%2F%2FaADRAsQABwAXALsAAAABAAUABCu7AAQAAQABAAQrMDEXESM1MxEjNZN0srJpAv4v%2FKQvAAEAIv9oARECxAAxACsAuwAAAAEAAQAEK7sAHAABAB0ABCu7ABAAAQAPAAQrugAoAA8AEBESOTAxBRUjIiY1ND4CNTQuAiM1Mj4CNTQmNTQ2OwEVIyIGFRQWFRQGBxUeARUUBhUUFjMBES07OgMDAwgTIhkZIhMICTo7LRspGwYcICAcBhspaS84TRsxLi4ZDxsWDjQOFRwOM1g3TTgvKjEuVDMxMwkECTQwM1QuMSoAAQAf%2F2gBDQLEADMAKwC7AAAAAQAxAAQruwAWAAEAEwAEK7sAIQABACIABCu6AAoAIgAhERI5MDEXMjY1NCY1NDY3NS4BNTQ2NTQmKwE1MzIeAhUUBhUUFhcVIg4CFRQeAhUUDgIrATU5KRsFGyAgGwUbKRosHiwdDgkkMhkhFAgDAwMOHSweLGkqMS5UMzA0CQQJMzEzVC4xKi8NHjMnN1gzHS8BNA4WGw8ZLi4xGyczHg0vAAAAAAEACv9gAVECxgADABgAuAAARVi4AAAvG7kAAAASPlm4AALcMDEBMwEjARU8%2FvU8Asb8mgABAFz%2FBgCWAu4AAwALALoAAQACAAMrMDETMxEjXDo6Au78GAABAA7%2FYAFUAsYAAwAYALgAAEVYuAAALxu5AAAAEj5ZuAAC3DAxEzMBIw47AQs7Asb8mgAAAgBc%2FwYAlgLuAAMABwALALoAAQAFAAMrMDETMxEjFxEjEVw6Ojo6Au7%2BNU3%2BMAHQAAAAAAEAOgGkAWgCyAAOABQAuAAARVi4AAUvG7kABQASPlkwMRM3JzcXNzMXNxcHFwcnB2I5YQ9mCTEJZw9hOCdHRwHBXiguGWxrGC4oXh1WVgAAAgAt%2F8ABxAKsAA8ARwAXALsALwABACgABCu7AEQAAQATAAQrMDElPgE1NC4CJw4BFRQeAhMuASMiBhUUHgQVFAYHHgEVFA4CIyImJzceATMyNjU0LgQ1NDY3LgE1ND4CMzIWFwFAHR8oPEcfHSAoPUdMGDYhKiUpPkc%2BKS8mDhAYKzsjNlgfMhk6KCgtKT1IPSkwJg8REyc4JjBNHcEOJiEiLCEcEhAoHyErIB0BaxQaJRobJB4eKz0uMDsWEScaHjIkFSYhLRgcKB0cJh4dKj0uLEAVECgaGi8kFSIXAAACACn%2FsAHQApAAAwAQACUAuAAARVi4AAAvG7kAAAAQPlm4AABFWLgADi8buQAOABA%2BWTAxATMRIwMiLgI1ND4COwERAXxUVFc2XUMmJEFYNCwCkP0gATIZNVI5O1EzFv5SAAAAAwAx%2F%2FUCtwKNABMAJwBFADMAuwA6AAEAQQAEK7sALQABADQABCu4AC0QuAAj3LkABQAB9LgAQRC4ABncuQAPAAH0MDETND4CMzIeAhUUDgIjIi4CNxQeAjMyPgI1NC4CIyIOAhc0PgIzMhYXBy4BIyIGFRQWMzI2NxcOASMiLgIxNFh2QUF1WTQ0WXVBQXZYNC8sS2Q5OWRLLCxLZDk5ZEssZh8zQyQqOxgjFCkaN0NBNiAwFh4cPi0mQjIcAUNMelYuLlZ6TE17Vy8vV3tNQmtNKipNa0JBa0wpKUxrQStGMhohGCcUFUs7Qk0ZEyoYIRszSQAAAAAEABcBPwGQAskAEwAnADUAPQBXALgANC%2B4ACkvuAA0ELgAFNy5AAAAAfS4ACkQuAAe3LkACgAB9LoAMgA0ACkREjm4ADIvuQA2AAH0ugAvADYAMhESObgANBC4ADHQuAApELkAOwAB9DAxEyIuAjU0PgIzMh4CFRQOAicyPgI1NC4CIyIOAhUUHgIDMzIWFRQGBxcjJyMVIzcyNTQmKwEV0ydEMx4eM0QnJ0UzHh4zRScfNygXFyg3HyA2JxcXJzYoTCAuFBEuLiMpKUMrEhccAT8dNEgsLEg0HR00SCwsSDQdJRcqOyQjOysYGCs7IyQ7KhcBCB0kEh8GU0ZGZiIPEkMAAAIAM%2F9lAxwChgBFAFQAPwC7ADsAAQBBAAQruwAnAAEADwAEK7sASQABABYABCu7AB4AAQBQAAQruwAFAAEAMQAEK7oAIgBQAB4REjkwMTc0PgIzMh4CFRQOAiMiJicjDgEjIiY1ND4CMzIWFzM3MwcGMzI%2BAjU0LgIjIg4CFRQeAjMyNjcXBiMiLgIlFBYzMjY%2FAS4BIyIOAjNDc5dUTHlVLiU6RyIpOQUCGUAhM0UbMkcsGigOAgs3Jx5UGC8nGCNGaERDfmI7LVBtQC5SIhZVaUqAXzcBCCgeFS0aHQ4eFB4vIRHLZKR0PzBXekpCY0MiJiYdJ0hFKFNDKhcZKMh1HDVNMTxnSio3ZI5YSXJOKRkUMTMuW4ZXMCocH58XEyAyPAACACMAAAHTAooAGwAfAJsAuAAARVi4AAgvG7kACAAOPlm4AABFWLgADC8buQAMAA4%2BWbgAAEVYuAAWLxu5ABYABD5ZuAAARVi4ABovG7kAGgAEPlm7AAMAAQAAAAQruwAHAAEABAAEK7gABxC4AArQuAAHELgADtC4AAQQuAAQ0LgAAxC4ABLQuAAAELgAFNC4AAAQuAAY0LgAAxC4ABzQuAAEELgAHdAwMTcjNTM3IzUzNzMHMzczBzMVIwczFSMHIzcjByMTNyMHc1BXElVcFzUXhRg1GFFXElVcGTUYhBk22hKEEsw5lDq3t7e3OpQ5zMzMAQWUlAAAAP%2F%2FAFcBuADsAz4CBwC3AAABuAAA%2F%2F8AKAG4AUADSgIHALgAAAG4AAD%2F%2FwAjAawBPwNKAgcAuQAAAbgAAP%2F%2FACoBuAFQAz4CBwC6AAABuAAAAAIAI%2F%2F0AU0BkgALABcAKAC4AAYvuAAARVi4AAAvG7kAAAAEPlm5AAwAAfS4AAYQuQASAAH0MDEXIiY1NDYzMhYVFAYnMjY1NCYjIgYVFBa4RFFRRENSUkMmMDAmJzAwDGxkY2trY2RsM09OTk1NTk5PAAAAAQBXAAAA7AGGAAgAIgC4AAYvuAAARVi4AAgvG7kACAAEPlm4AAYQuQAAAAH0MDETIzU%2BATczESOsVSEsFDRAATQqBhMP%2FnoAAQAoAAABQAGSABoALAC4AA8vuAAARVi4ABkvG7kAGQAEPlm5ABcAAfS4AADQuAAPELkACAAB9DAxNz4DNTQmIyIGByc%2BATMyFhUUDgIHMxUhNC1GLhgoIxkqESYXQyg7RxYnNR%2Bl%2FvQlKUE2LxYmLCEYIyIqQD4cNDU4IDcAAAABACP%2F9AE%2FAZIAKgA%2BALgAFy%2B4AABFWLgAJy8buQAnAAQ%2BWboACgAJAAMruAAnELkAAwAB9LgAFxC5ABAAAfS6AB8ACQAKERI5MDE3HgEzMjY1NCYjNTI2NTQmIyIGByc%2BATMyHgIVFAYHHgEVFA4CIyImJ04SMh8gLkA5MzcnIBYoEScaPSkZLSIUJh4hMxYmMxwwShdhGx8kIiIjKSgeHCIbFCIdIw4bJxkjLw4IMScbKx8QKyEAAgAqAAABUAGGAAUAEABMALgADi%2B4AABFWLgACS8buQAJAAQ%2BWbsAAAABAAoABCu4AA4QuQACAAH0uAAAELgABdC4AAoQuAAH0LgABRC4AAzQuAAAELgAD9AwMTc1NyMPARcjFSM1IzU3MxUz3AQEMj3jOjqypEg6lkZtUWIuaGgh%2FfD%2F%2FwAlAQIBKgJUAgYAvQAA%2F%2F8AHgECAU4CVAIGAL4AAAACACUBAgEqAlQAGQAiADsAuAAAL7gAEC%2B7AAYAAQAdAAQruAAQELkACQAB9LgAABC4ABXQuAAAELkAGgAB9LoAFwAAABoREjkwMRMiJjU0NjcuASMiBgcnPgEzMhYdASMnIw4BJzI3NQ4BFRQWiC02X2gBGiMaNxQXGUUnPDcyBwQUMg0nK009HgECNCs1NwogKhUNKhAbRkDEJRIbMihVCSYcGhgAAAACAB4BAgFOAlQAEwAfABsAuAAKL7gAAC%2B5ABQAAfS4AAoQuQAaAAH0MDETIi4CNTQ%2BAjMyHgIVFA4CJzI2NTQmIyIGFRQWth83KhgYKjcfHzcqGBgqNx8qLi4qKi8vAQIXKz8oKD8rFxcrPygoPysXM0E1NkBANjVBAAAAAgApAa0BIwKtABMAHwAXALsAFAABAAAABCu7AAoAAQAaAAQrMDETIi4CNTQ%2BAjMyHgIVFA4CJzI2NTQmIyIGFRQWphktIxQUIy0ZGS0jFBQjLRkhKiohISoqAa0SIS8dHi8iEhIiLx4dLyESLi4jJS4uJSMuAAAAAgAaAGcB1wItACEANQAoALgAAEVYuAAMLxu5AAwACj5ZuwAnAAEAHgAEK7gADBC5ADEAAfQwMT8BLgE1NDY3JzcXNjMyFhc3FwceARUUBgcXBycOASMiJwc3FB4CMzI%2BAjU0LgIjIg4CGkARExMRQCxEMD8dOhdELEERFBQRQSxEFzodQC9EPBMgLBgYKyATEyArGBgsIBOUQRc6IyM7F0ItRiUTEkYtQhc7IyM6F0EtRRMTJkXiHjEkExMkMR4eMSQTEyQxAAABADT%2FkgG1AuwALQBdALgAAEVYuAAnLxu5ACcADj5ZuAAARVi4ABMvG7kAEwAEPlm4ACcQuQADAAH0ugAGABMAJxESObgAExC4ABDQuAATELkAGgAB9LoAHQAnABMREjm4ACcQuAAq0DAxAS4BIyIGFRQeBBUUBgcVIzUuASc3HgEzMjY1NC4ENTQ2NzUzFR4BFwF8HDUpLjYpPkk%2BKVNIPDBaICYgTS44Nyk%2BST4pT0I8MEMbAgwbHjQsJC4jIS9FNkhcCmVjBSsdORwnOC8oNSciLD8xQ1kLZGMFKh0AAAEANQAAAcUCigAsAFkAuAAARVi4ABUvG7kAFQAOPlm4AABFWLgAAi8buQACAAQ%2BWbkAAAAB9LoAJAAVAAIREjm4ACQvuAAK0LgAJBC5ACMAAfS4AAvQuAALL7gAFRC5ABwAAfQwMSUVITU%2BATU0JicjNTczLgE1ND4CMzIWFwcuASMiBhUUFhczFSMeARUUBgcVAcX%2BcTM3BANkQxIKERsxRCo2SxowEzAiNjkPCZ%2BSAgMgHkdHMhxfOQ4bDjQEID0gKkQwGisgLxceQTQgOyA4DhsPNUYfBAAAAAABABcAAAHaAn4AHQCEALgAAEVYuAAdLxu5AB0ADj5ZuAAARVi4AAkvG7kACQAOPlm4AABFWLgAFC8buQAUAAQ%2BWboABAAdABQREjm6ABkAHQAUERI5uAAZL7kADAAB9LgAGRC4AA3QuAAZELgAGNC4ABgvuAAQ0LgAGBC5ABUAAfS4ABHQuAAZELkAGwAB9DAxExceARczPgE%2FATMDMxUjFTMVIxUjNSM1MzUjNTMDbU4PHRAEER0PTlSkjqOjo1KioqKNowJ%2BqyFDIyNDIav%2BwC9BMJ6eMEEvAUAAAQAX%2F%2FQB6wKKADUAbQC4AABFWLgAGS8buQAZAA4%2BWbgAAEVYuAADLxu5AAMABD5ZuwAtAAEALgAEK7gALhC4AAnQuAAtELgACtC4AC0QuAAl3LgAEtC4ACUQuQAkAAH0uAAT0LgAGRC5ACAAAfS4AAMQuQAyAAH0MDElDgEjIi4CJyM1NyY0NTwBNyM1Nz4DMzIWFwcuASMiBgczFSEGFBUcARczFSMeATMyNjcB6yFUNy1NPCkJQDsBATtACSo%2FUzEtThoxFTIgQlEM%2Fv7%2BAQHa1Q1NPiU3GlEsMSFAWzsrBAkSCQgQCCwFO11BIi0hLxohYlcxBw4IChMJMFVgJCMAAAACAD3%2F3wHGAo0ABgAlADcAuwAiAAEACgAEK7sAGgABACEABCu4ACEQuAAA0LgAIhC4AAbQuAAKELgADdC4ABoQuAAX0DAxAQ4BFRQWHwEOAQcVIzUuAzU0PgI3NTMVHgEXBy4BJxE%2BATcBBjdAPjnAHUgnNC1KNR0fNkoqNCxAFygULRogNBQB3Q1YQkNYDQkaIgNnaAUlPFQ1NFI8JQZqZwIiFjQSFgL%2BqAIbEgAAAAH%2FWf%2F0APsCnAADABgAuAAARVi4AAAvG7kAAAAEPlm4AAHcMDEHATMBpwFqOP6WDAKo%2FVgAAAD%2F%2F%2F9Z%2F%2FQA%2BwKcAgYAxgAA%2F%2F8AI%2F%2F0AxYCnAAnALYAAAEKACcAxgFxAAAABwC2AckAAAAA%2F%2F8AQP%2F0Au0CnAAnALf%2F6QEKACcAxgFbAAAABwC6AZ0AAAAA%2F%2F8AQP%2F0AvkCnAAnALf%2F6QEKACcAxgFGAAAABwC4AbkAAAAA%2F%2F8AI%2F%2F0AvwCnAAnALkAAAEKACcAxgGAAAAABwC6AawAAAAAAAEAIgBoAc8CLAALAB0AuwADAAEAAAAEK7gAAxC4AAbQuAAAELgACNAwMRMjNTM1MxUzFSMVI9i2tkG2tkEBKz7Dwz7DAAAAAAEAIgErAc8BaQADAA0AuwABAAEAAgAEKzAxEyEVISIBrf5TAWk%2BAAABADIAfgG%2FAhUACwALALoABQAJAAMrMDE%2FASc3FzcXBxcHJwcym5ssm5osm5ssmpurn54tn58tnp8toKAAAAAAAwAiAGABzwIzAAsAFwAbACEAuwAPAAEAFQAEK7sABgABAAAABCu7ABkAAQAaAAQrMDETIiY1NDYzMhYVFAYDNDYzMhYVFAYjIiYnIRUh%2BRcgIBcXHx9OIBcXHx8XFyCgAa3%2BUwHIHhgXHh4XGB7%2BzhceHhcYHh7rPgAA%2F%2F8AIgDBAc8B1AImAM0AawAGAM0AlgAAAAEAIgCDAc8CFQAJABUAugABAAgAAyu6AAUACAABERI5MDETJRUPARUfARUlIgGt04aG0%2F5TAW2oR04yBDJOR6gAAAAAAQAiAIMBzwIVAAkAFQC6AAgAAQADK7oABQABAAgREjkwMQEFNT8BNS8BNQUBz%2F5T04aG0wGtASuoR04yBDJOR6gAAAACACIAAAHPAiwACwAPADgAuAAARVi4AA4vG7kADgAEPlm7AAMAAQAAAAQruAADELgABtC4AAAQuAAI0LgADhC5AAwAAfQwMRMjNTM1MxUzFSMVIwchFSHYtrZBtrZBtgGt%2FlMBMD6%2Bvj6xQT4AAAAAAQA8ARwBtQKeAAkAGgC4AABFWLgAAC8buQAAABA%2BWbkABQAB9DAxEzMTIy8BIw8BI9RJmEhBMQQyQUgCnv5%2BsIWFsAABACQBAQHNAZMAFwAnALsACAABAA8ABCu4AA8QuAAU3LkAAwAB9LgAC9C4AA8QuAAX0DAxEz4BMzIeAjMyNjcXDgEjIi4CIyIGByQbQiAeLykmFRYmES4bQiAeLykmFRYmEQE5MCoaIBodICIwKRogGh0gAAEAIgBoAc8BaQAFAA0AuwABAAEABAAEKzAxEyERIzUhIgGtQv6VAWn%2B%2F8MAAQBS%2FzgB1AHmABcAJAC4AABFWLgAES8buQARAAQ%2BWbkABQAB9LoAFAARAAUREjkwMRMzERQWMzI2NxEzESMnIw4BIyImJxcVI1JSLDAmOSNSRAcCHUUqHS4RBVIB5v7XRT0nKwFZ%2FhpMJy0RGlqR%2F%2F8AoAI9AVACygAHAOABDwAAAAD%2F%2FwDOAj0BfgLKAAcA4gEPAAAAAP%2F%2FAI4CPQGQAsoABwDkAQ8AAAAA%2F%2F8AhAJDAZoCrQAHAOYBDwAAAAD%2F%2FwCIAkwBlgKuAAcA6QEPAAAAAP%2F%2FAJQCWQGKApIABwDoAQ8AAAAA%2F%2F8AsgIeAWwC1wAHAOsBDwAAAAD%2F%2FwDA%2FysBWQADAAcA7QEVAAAAAAAB%2F5ECPQBBAsoAAwAYALgAAEVYuAADLxu5AAMADD5ZuAAB3DAxAzMXI29WWj8Cyo0AAAAAAf99AsIAMwMyAAMACwC6AAEAAwADKzAxAzMXI4NeWEUDMnAAAf%2B%2FAj0AbwLKAAMAGAC4AABFWLgAAC8buQAAAAw%2BWbgAAtwwMQMjNzMCP1pWAj2NAAAAAAH%2FzQLCAIMDMgADAAsAugACAAAAAyswMRMjNzMSRVheAsJwAAH%2FfwI9AIECygAHADcAuAAARVi4AAYvG7kABgAMPlm4AABFWLgAAy8buQADAAw%2BWbgABhC4AADcugAFAAAABhESOTAxAzMXIycjByMiRF88QwRDPALKjVtbAAAAAf94AsIAiAMyAAcAFwC6AAYAAQADK7gABhC4AAfcuAAD0DAxAzczFyMnIweIYFBgQ0MEQwLCcHBERAAAAf91AkMAiwKtABYAQQC4AABFWLgAAC8buQAAAAw%2BWbgAAEVYuAAOLxu5AA4ADD5ZuwATAAEAAwAEK7gADhC5AAgAAfS4AAMQuAAL0DAxAz4BMzIeAjMyNzMOASMiLgIjIgcjiwQoJRMgGxgMHAkuBCglEx8bGA0cCS4CQy09EBMQMy09EBMQMwAAAAAB%2F28CxwCRAzMAFwAjALoAAAARAAMrugAFAAwAAyu4AAAQuAAI0LgADBC4ABTQMDETIi4CIyIGByM%2BATMyHgIzMjY3Mw4BPBQgGxoODhYELgUsJBQgGxoODhYELgUsAscQFBAaGi89EBQQGxkuPgAAAf%2BFAlkAewKSAAMACwC4AAMvuAAB3DAxAzMVI3v29gKSOQAAAv95AkwAhwKuAAsAFwAoALgAAEVYuAAALxu5AAAADD5ZuAAG3LgAABC4AAzQuAAGELgAEtAwMQMiJjU0NjMyFhUUBjMiJjU0NjMyFhUUBlYWGxsWFRwclxUcHBUWGxsCTBwVFRwcFRUcHBUVHBwVFRwAAAAC%2F3kCywCHAy0ACwAXABsAugAGAAAAAyu4AAAQuAAM0LgABhC4ABLQMDEDIiY1NDYzMhYVFAYzIiY1NDYzMhYVFAZWFhsbFhUcHJcVHBwVFhsbAsscFRYbGxYVHBwVFhsbFhUcAAAAAAL%2FowIeAF0C1wALABcAEwC6AAwAAAADK7oABgASAAMrMDERIiY1NDYzMhYVFAYnMjY1NCYjIgYVFBYpNDQpKTQ0KRQcHBQUHBwCHjMqKjIyKiozJR4aGR4eGRoeAAAAAAL%2FowK7AF0DawALABcAEwC6AAwAAAADK7oABgASAAMrMDERIiY1NDYzMhYVFAYnMjY1NCYjIgYVFBYpNDQpKDU1KBMcHBMUHBwCuy8pKDAwKCkvJBsZFxwcFxkbAAAAAAH%2Fq%2F8rAEQAAwARABMAugACABEAAyu6AAsACgADKzAxJzMHHgEVFA4CByc%2BATU0JicTNRkYIxgoNRwIKDEhHgM1CCAfFiAWDQMpBRcUFBUIAAAAAAH%2Fq%2F8rAEQAAwARABMAugARAAIAAyu6AAoACwADKzAxJzMHHgEVFA4CByc%2BATU0JicTNRkYIxgoNRwIKDEhHgM1CCAfFiAWDQMpBRcUFBUIAAAA%2F%2F8AQwAAALUCtAIGACYAAAABAAAA8QBaAAcAcQAFAAEAAAAAAAoAAAIAAXMAAwABAAAAYgBiAGIAYgCwARYBZgGiAeACFgJ0ArAC0AMCA0oDcAPUBCQEeATABTIFhgX0BiAGZgaiBxgHdAeyB%2BgIXgjaCSoJogoEClILAgtQC4QLzAwUDEYMxg0cDXAN7A5iDqoPFg9gD7YP8hBoEMIRGBFOEVoRZhFyEX4RihGWEfoSBhISEh4SKhI2EkISThJaEmYSchJ%2BEooSlhKiEq4TOhOQE5wTqBO0E8ATzBQkFGgUdBSAFIwUmBSkFLAVZhVyFX4VihWWFaIVrhW6FcYV0hXyFf4WChYWFiIWLhY6FqYXPhe8F8gX1BfgF%2BwX%2BBgEGIYY7BlkGeYamhriGxwbaBvaHCociBzuHSQdsB4WHjgeYh5uHnoeqB7UHyIfch%2BIH5Qfuh%2FgH%2Bwf%2BCACIBAgKCBAIEwgWCBsIHQgiCCcIKYgzCDgIQIhJCFAIVwhtCIQIioiPCJWInAimCMIIzojtiQ4JMolSCVSJVwlZiVwJaolziYOJmomrCa0JrwnECdOJ4on7iheKMwpOim8KhQqMCo4KkoqXCpuKoAqpCq4KtgrFisiK0QrZiueK8Ar%2BiwQLEgsUixcLGYscCx6LIQsjiyYLLIsxCzeLPAtHi08LYItui3MLgYuOi5qLpouxC7uLu4u9gABAAAAAQzMdwyY3F8PPPUACQPo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AdAAH0ugAlAC4AFhESOTAxNx4BMzI2NTQuAi8BLgM1ND4CMzIWFwcuASMiBhUUHgIfAR4BFRQOAiMiJid3I1UpLi0NGSMVVRkvJRYiPlUzOW8qSyBAJyYtDxskFVQ8RSE%2FXDpAfzK3HiYiHQ8WEQ8JJAogKzkkKEc1HiwqXRkbHxwPFREPCSIYVEUpSTchLy8AAAEAGQAAAhMCjAAHADMAuAAARVi4AAIvG7kAAgAQPlm4AABFWLgABy8buQAHAAQ%2BWbgAAhC5AAAAAfS4AAXQMDETIzUhFSMRI8yzAfqzlAIQfHz98AAAAAABAEn%2F9AJPAowAEQA8ALgAAEVYuAAALxu5AAAAED5ZuAAARVi4AAkvG7kACQAQPlm4AABFWLgADi8buQAOAAQ%2BWbkABQAB9DAxEzMRFBYzMjY1ETMRFAYjIiY1SZQ6Nzc8joN%2Bf4YCjP6TYExMYAFt%2FqKklpakAAAB%2F%2FkAAAIzAowADQBAALgAAEVYuAAALxu5AAAAED5ZuAAARVi4AAovG7kACgAQPlm4AABFWLgADS8buQANAAQ%2BWboABQAAAA0REjkwMQMzEx4BFzM%2BATcTMwMjB5xODxcPBA4YDk2WxLECjP7QNmU2NmU2ATD9dAABAA4AAAMgAowAIQB2ALgAAEVYuAAALxu5AAAAED5ZuAAARVi4AAovG7kACgAQPlm4AABFWLgAFC8buQAUABA%2BWbgAAEVYuAAhLxu5ACEABD5ZuAAARVi4ABcvG7kAFwAEPlm6AAUAAAAhERI5ugAPABQAFxESOboAHAAhAAoREjkwMRMzEx4BFzM%2BATcTMxMeARczPgE3EzMDIwMuAScjDgEHAyMOly0GDgYEChMKRn1GChMKBAcNBy2NdLlACA0FBAYNCD22Aoz%2BzzNnNDRnMwEx%2Fs8yZzU1ZjMBMf10ASgmTSQkTSb%2B2AAAAAEACwAAAiwCjAAZAFsAuAAARVi4AAEvG7kAAQAQPlm4AABFWLgACy8buQALABA%2BWbgAAEVYuAAZLxu5ABkABD5ZuAAARVi4AA8vG7kADwAEPlm6AAYAAQAZERI5ugATAA8ACxESOTAxEwMzFx4BFzM%2BAT8BMwMTIycuAScjDgEPASPAqqQ5CxYOBAsVCjSdqbSkQQwXDgQLFgs9ngFPAT17FzMfHzMXe%2F68%2FriFGjMeHjMahQAAAAAB%2F%2FgAAAIVAowADwBAALgAAEVYuAABLxu5AAEAED5ZuAAARVi4AAsvG7kACwAQPlm4AABFWLgADy8buQAPAAQ%2BWboABgABAA8REjkwMTcDMxceARczPgE%2FATMDFSO9xZ46DhkOBA4bDjuaxZPoAaSWJUUmJkUllv5c6AAAAQAkAAAB%2FAKMAAkAPQC4AABFWLgAAy8buQADABA%2BWbgAAEVYuAAILxu5AAgABD5ZuQAGAAH0uAAA0LgAAxC5AAEAAfS4AAXQMDE3ASE1IRUBIRUhJAEg%2FvsBuv7gASP%2BKFkBt3xZ%2Fkl8AAAAAgAv%2F%2FQB2QH8ABsAJQB2ALgAAEVYuAANLxu5AA0ACD5ZuAAARVi4ABcvG7kAFwAEPlm4AABFWLgAEi8buQASAAQ%2BWboAAwANABcREjm4AAMvuAANELkABgAB9LoAEwASAA0REjm4ABcQuQAfAAH0uAATELkAIgAB9LgAAxC5ACMAAfQwMTc0NjcuASMiBgcnPgEzMhYVESMnIw4BIyIuAjcUFjMyNjc1DgEvhJMCJCggQCQ0MGk7YGV4CwMgRyoiNiYVjB8aGiQUTzyKTlgPIScYFWEdJG1z%2FuQzHCMXKTYrGBcWFFcLKgAAAAACAEH%2F9AIWAr0AFgAhAIMAuAAARVi4AAAvG7kAAAASPlm4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAQLxu5ABAABD5ZuAAARVi4ABYvG7kAFgAEPlm6AAMABgAQERI5ugATABAABhESObgAExC5ABcAAfS4ABAQuQAaAAH0uAAGELkAHwAB9LgAAxC5ACEAAfQwMRMzFQc%2BATMyHgIVFA4CIyImJyMHIzceATMyNjU0IyIHQZMEHUMjLUgzGyM7SyghQx0EDHOTFCgTJjZWLCkCvaxMGh0kQV05QGRFJCAgNIwSDkVNhi0AAQAk%2F%2FQBvgH8ABwAOQC4AABFWLgABS8buQAFAAg%2BWbgAAEVYuAAYLxu5ABgABD5ZuAAFELkACwAB9LgAGBC5ABEAAfQwMTc0PgIzMhYXByYjIgYVFBYzMjY3Fw4BIyIuAiQqR14zLkoZQyMiNj4%2FMBssEjskWCk0WUIm%2BD5hQiMfGlsdTEFBTBYOXSAeI0JhAAAAAAIAJ%2F%2F0AfwCvQAWACMAgwC4AABFWLgABS8buQAFAAg%2BWbgAAEVYuAAKLxu5AAoAEj5ZuAAARVi4ABIvG7kAEgAEPlm4AABFWLgADS8buQANAAQ%2BWboACAASAAUREjm6AA4ABQASERI5uAASELkAGgAB9LgADhC5AB0AAfS4AAgQuQAeAAH0uAAFELkAIQAB9DAxNzQ%2BAjMyFhcnNTMRIycjDgEjIi4CNxQWMzI2NzUuASMiBicjOUsnKjYaBpN4CgQaRiQuSzUdly8rGCcSFCoUIzb4PWBDJBwYTKn9QzEaIyRDYD9KRBQZyxIOQwAAAAIAJP%2F0AeEB%2FAAbACIAUQC4AABFWLgABS8buQAFAAg%2BWbgAAEVYuAAXLxu5ABcABD5ZugANAAUAFxESObgADS%2B4ABcQuQARAAH0uAAFELkAHwAB9LgADRC5ACIAAfQwMTc0PgIzMh4CFRQGByEeATMyNxcOASMiLgIlNCYjIgYHJChBVC01TzUaBAL%2B1wpFMjU2MSZdLTVcRCYBQCYtIzUI%2BDxhQyQkQFczFCEJOTMhWRoeI0NgcSs2LzIAAQAYAAABdALJABgAVgC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAVLxu5ABUAEj5ZuAAARVi4AAovG7kACgAEPlm4ABUQuQACAAH0uAAGELkACQAB9LgADNC4AAYQuAAP0DAxASYjIgYdATMVIxEjESM1NzU0PgIzMhYXAVkcFxseWVmTQkITLEYyHzMRAksKISYec%2F6DAX1tBRsnRjQeDAYAAAADACL%2FLQIHAfwADgBDAE8AbQC4AABFWLgAIS8buQAhAAg%2BWbgAAEVYuAA%2FLxu5AD8ABj5ZuQADAAH0ugA2ACEAPxESObgANi%2B5AAkAAfS4ACEQuAAk0LgAJC%2B5ACUAAfS4ADYQuAAu0LgALi%2B5AEQAAfS4ACEQuQBKAAH0MDEXFBYzMjY1NCYrASImJwYHNDc1LgE1NDY3NS4BNTQ%2BAjMyFzMVIx4BFRQOAiMiJw4BFRQWOwEyFhUUDgIjIi4CEzI2NTQmIyIGFRQWmzwyMkAnJTMaIAwbeUcUGiAYGikiOUooLCG1TwcJHjVHKR0fCgghKlBcYSdIZkAsSzkg2x0nJx0dJyc%2BHB0jGhcQAwMYMDwoBA0oHxsxEQQSQywtQy0WDGsLIhQrPykUCggQDRMSO0MnQS8bDx4wAWwpKicpKCgqKQABAEEAAAIAAr0AFABYALgAAEVYuAAGLxu5AAYACD5ZuAAARVi4AAAvG7kAAAASPlm4AABFWLgAFC8buQAUAAQ%2BWboAAwAGABQREjm4AAvQuAAGELkADwAB9LgAAxC5ABIAAfQwMRMzFQc%2BATMyFhURIxE0JiMiBgcRI0GTBxxKM1FJkx0gHCgYkwK9rFkZK2pe%2FswBITYoGRf%2BsQACADUAAADfAtIACwAPAC0AuAAARVi4AAwvG7kADAAIPlm4AABFWLgADi8buQAOAAQ%2BWboABgAAAAMrMDETIiY1NDYzMhYVFAYHMxEjiiUwMCUmLy9vk5MCOSsiIioqIiIrSf4QAAAAAAL%2Fzf88AOEC0gARAB0ANwC4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAAHLxu5AAcABj5ZugAYABIAAyu4AAcQuQAOAAH0MDETMxEUDgIjIiYnNx4BMzI2NRMiJjU0NjMyFhUUBkKTESlEMx8pDxoKEgsdF0olMDAlJTAwAfD%2BEylINiAIBmwDBCYqAjorIiIqKiIiKwABAEEAAAIeAr0ADABbALgAAEVYuAAELxu5AAQACD5ZuAAARVi4AAAvG7kAAAASPlm4AABFWLgADC8buQAMAAQ%2BWbgAAEVYuAAILxu5AAgABD5ZugACAAAADBESOboACQAAAAgREjkwMRMzETM3MwcTIycHFSNBjwSdoK67n3A%2FjwK9%2Fm7FzP7cwUd6AAEAQf%2F0ARICvQARACsAuAAARVi4AAAvG7kAAAASPlm4AABFWLgADC8buQAMAAQ%2BWbkABQAB9DAxEzMRFBYzOgE3Fw4BIyIuAjVBkxEJBQcGEgwlGSY0Hw4Cvf3XFxICbQUHGCs9JgAAAQBBAAADHQH8ACEAmAC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAALxu5AAAACD5ZuAAARVi4ACEvG7kAIQAEPlm4AABFWLgAGS8buQAZAAQ%2BWbgAAEVYuAARLxu5ABEABD5ZugACAAAAIRESOboACQAAACEREjm4AAYQuAAM0LkAFQAB9LgACRC5ABcAAfS4AAYQuQAdAAH0uAACELkAHwAB9DAxEzMXMz4BMzIWFz4BMzIWFREjETQmIyIHESMRNCYjIgcRI0F4CgQfRjE1QRMhSTJQS5MdICUwkx0gJi6TAfBAHy0rKCIxa13%2BzAEhNigw%2FrEBITYoMP6xAAAAAAEAQQAAAgAB%2FAAUAGUAuAAARVi4AAYvG7kABgAIPlm4AABFWLgAAC8buQAAAAg%2BWbgAAEVYuAAULxu5ABQABD5ZuAAARVi4AAsvG7kACwAEPlm6AAIAAAAUERI5uAAGELkADwAB9LgAAhC5ABIAAfQwMRMzFzM%2BATMyFhURIxE0JiMiBgcRI0F4CgQgTDNRSZMdIBwoGJMB8D8eLWpe%2FswBITYoGRf%2BsQAAAAACACT%2F9AIHAfwAEwAfADUAuAAARVi4AAUvG7kABQAIPlm4AABFWLgADy8buQAPAAQ%2BWbkAFwAB9LgABRC5AB0AAfQwMTc0PgIzMh4CFRQOAiMiLgI3FBYzMjY1NCYjIgYkKENYLy9XQygoQ1cvL1hDKJctLi0uLi0uLfg%2BYUIjI0JhPj5hQiMjQmE%2BQUxMQUFMTAAAAgBB%2F0gCFgH8ABYAIQCDALgAAEVYuAAJLxu5AAkACD5ZuAAARVi4AAMvG7kAAwAIPlm4AABFWLgAAi8buQACAAY%2BWbgAAEVYuAATLxu5ABMABD5ZugAFAAkAExESOboAFgATAAkREjm4ABYQuQAXAAH0uAATELkAGgAB9LgACRC5AB8AAfS4AAUQuQAhAAH0MDEXFSMRMxczPgEzMh4CFRQOAiMiJic3HgEzMjY1NCMiB9STeAoEHUknLUgyGyM7SyggPBoFFCgTJjZWKyonkQKoMRojJEJdOUBjRSQbGWQSDkVNhi0AAAIAJ%2F9IAfwB%2FAAWACMAfwC4AABFWLgABS8buQAFAAg%2BWbgAAEVYuAALLxu5AAsACD5ZuAAARVi4AA0vG7kADQAGPlm4AABFWLgAEi8buQASAAQ%2BWboACAAFABIREjm6AA8AEgAFERI5uQAaAAH0uAAPELkAHQAB9LgACBC5AB4AAfS4AAUQuQAhAAH0MDE3ND4CMzIWFzM3MxEjNTcOASMiLgI3FBYzMjY3NS4BIyIGJyM5SycpPh0EDHOTBhlCIi5LNR2XLysYJxIUKhQjNvg9YEMkHSAx%2FViXTBgfJENgP0pEFBnLEg5DAAAAAQBBAAABjwH8ABIAUgC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAALxu5AAAACD5ZuAAARVi4ABIvG7kAEgAEPlm6AAIAAAASERI5uAAGELgADdy4AAIQuQAQAAH0MDETMxczPgEzMhYXBy4BIyIGBxEjQXgKBBtMJhUbCxgOGBAcPRSTAfBXMzAFBX8EBCgz%2FuAAAAEAFf%2F0AZ8B%2FAAwAEkAuAAARVi4ABUvG7kAFQAIPlm4AABFWLgALS8buQAtAAQ%2BWbkAAwAB9LoACwAtABUREjm4ABUQuQAcAAH0ugAjABUALRESOTAxNx4BMzI2NTQuAicuAzU0PgIzMhYXBy4BIyIVFB4CFx4DFRQOAiMiJidXIj4gIR4RHSQTFy4mGBsyRis5ViBCGzQaOBAbIxMYLycYGjRMMjFnJpUaGhYUDBMQDQgJGCMvHyI6KBcnGFgUFicMEQ4NBwkXIjEiIjorGSYfAAAAAQAR%2F%2FQBbgJ0ABkARQC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAULxu5ABQABD5ZuAAGELkACQAB9LgAANC4AAYQuAAD0LgAFBC5AA0AAfQwMRMjNT8BMxUzFSMVFBYzMjY3Fw4BIyIuAjVVREwRend3Ix0MGQoXFDYkLkEpEwF9bQaEhHPHKiUGBGsGDBwyRysAAAEAPP%2F0AfgB8AAUAGUAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgACS8buQAJAAg%2BWbgAAEVYuAARLxu5ABEABD5ZuAAARVi4AAwvG7kADAAEPlm4ABEQuQAFAAH0ugANAAkADBESObgADRC5AAgAAfQwMRMzERQWMzI2NxEzESMnIw4BIyImNTyTHiAcJhaTeAsDIEkzUUkB8P7fNigaHQFI%2FhBFJitqXgAAAAABAAwAAAH%2FAfAADQBAALgAAEVYuAAALxu5AAAACD5ZuAAARVi4AAovG7kACgAIPlm4AABFWLgADS8buQANAAQ%2BWboABQAAAA0REjkwMRMzFx4BFzM%2BAT8BMwMjDJRAChMKBAkTCkGNoqoB8OolTScnTSXq%2FhAAAAABABgAAALwAfAAIQB2ALgAAEVYuAAALxu5AAAACD5ZuAAARVi4AAovG7kACgAIPlm4AABFWLgAFC8buQAUAAg%2BWbgAAEVYuAAhLxu5ACEABD5ZuAAARVi4ABcvG7kAFwAEPlm6AAUAIQAAERI5ugAPABcAFBESOboAHAAAACAREjkwMRMzFx4BFzM%2BAT8BMxceARczPgE%2FATMDIycuAScjDgEPASMYkjAGCgYEBw0JN384CQ4IBAcJBy%2BId6wtCAwHBAcLByyoAfDmJUgmJkoj5uYlSCYmSCXm%2FhDGI0YoKEYjxgABAA4AAAH0AfAAGQBbALgAAEVYuAABLxu5AAEACD5ZuAAARVi4AAsvG7kACwAIPlm4AABFWLgAGS8buQAZAAQ%2BWbgAAEVYuAAPLxu5AA8ABD5ZugAGAAsADxESOboAFAABABkREjkwMRMnMxceARczPgE%2FATMHFyMnLgEnIw4BDwEjpo%2BeLAoVCwQIEggimJCZnjAMFwwECRQJJ5gBAu5QFSsVFSsVUP%2FxUhUsFRUrFlIAAAABAAz%2FPgH9AfAAHQBGALgAAEVYuAAILxu5AAgACD5ZuAAARVi4ABIvG7kAEgAIPlm4AABFWLgAGS8buQAZAAY%2BWbkAAwAB9LoADQAIABkREjkwMRceATMyNj8BAzMXHgEXMz4BPwEzAw4DIyImJzdMBxIIJSgKB7%2BURwsSCgQIEQk8jawSJzNBLBcgDxpIAgQkHRoB49UiRiUjRyPV%2FgsvRy8YBQVwAAABACYAAAG0AfAACQA9ALgAAEVYuAADLxu5AAMACD5ZuAAARVi4AAgvG7kACAAEPlm5AAYAAfS4AADQuAADELkAAQAB9LgABdAwMTcTIzUhFQMzFSEm0LkBcNDX%2FnJPAS5zTv7RcwAA%2F%2F%2F%2F%2BgAAAkMDNgImAAQAAAAHAOEBGwAA%2F%2F%2F%2F%2BgAAAkMDNgImAAQAAAAHAOMBGwAA%2F%2F%2F%2F%2BgAAAkMDNgImAAQAAAAHAOUBGwAA%2F%2F%2F%2F%2BgAAAkMDRwImAAQAAAAHAOcBGwAA%2F%2F%2F%2F%2BgAAAkMDSAImAAQAAAAHAOoBGwAA%2F%2F%2F%2F%2BgAAAkMDfQImAAQAAAAHAOwBGwAAAAL%2F8gAAAxkCjAAGABYAfAC4AABFWLgADi8buQAOABA%2BWbgAAEVYuAANLxu5AA0ABD5ZuAAARVi4AAkvG7kACQAEPlm6AAIADgANERI5ugAKAA4ADRESObgACi%2B5AAYAAfS4AAkQuQAHAAH0uAAOELkAEAAB9LoAFQAOAAkREjm4ABUvuQATAAH0MDEBESMOAQ8BBRUhNSMHIwEhFSMVMxUjFQGCBBQnFCoCFP5psUSbATYB5%2FrT0wEHARYwXitdi3yVlQKMfIN7lgAA%2F%2F8ALv8jAjACmAImAAYAAAAHAO4BXgAA%2F%2F8ATQAAAe8DNgImAAgAAAAHAOEBIAAA%2F%2F8ATQAAAe8DNgImAAgAAAAHAOMBIAAA%2F%2F8ATQAAAe8DNgImAAgAAAAHAOUBIAAA%2F%2F8ATQAAAe8DSAImAAgAAAAHAOoBIAAA%2F%2F%2F%2F9AAAAOEDNgImAAwAAAAHAOEAlgAA%2F%2F8ASwAAATgDNgImAAwAAAAHAOMAlgAA%2F%2F%2F%2F7gAAAT4DNgImAAwAAAAHAOUAlgAA%2F%2F%2F%2F7QAAAT8DSAImAAwAAAAHAOoAlgAA%2F%2F8ATQAAAkwDRwImABEAAAAHAOcBTQAA%2F%2F8ALv%2F0An4DNgImABIAAAAHAOEBVgAA%2F%2F8ALv%2F0An4DNgImABIAAAAHAOMBVgAA%2F%2F8ALv%2F0An4DNgImABIAAAAHAOUBVgAA%2F%2F8ALv%2F0An4DRwImABIAAAAHAOcBVgAA%2F%2F8ALv%2F0An4DSAImABIAAAAHAOoBVgAAAAMAKP%2FXApQCtQAHABAAKgCFALgAAEVYuAAmLxu5ACYAED5ZuAAARVi4ABkvG7kAGQAEPlm6AAAAGQAmERI5uQACAAH0ugAHACYAGRESOboACAAmABkREjm4ACYQuQAKAAH0ugAQABkAJhESOboAEQAmABkREjm6ABsAGQAmERI5ugAeABkAJhESOboAKAAmABkREjkwMSUWMzI2NTQvASYjIgYVFBYXAR4BFRQOAiMiJwcnNy4BNTQ%2BAjMyFzcXAQgiLUNOCTAkNENOBwYBdRodK05tQl5HPkxFHSErTm1CZklCTI8cc2MtJF0ibmIcMhUBNilpQVB%2BWC8wTTpWK3JFUH1VLTVSOwAAAAACAC4AAAMyAowAFAAhAFUAuAAARVi4AAUvG7kABQAQPlm4AABFWLgAEC8buQAQAAQ%2BWbgABRC5ABwAAfS4AAfQugAMAAUAEBESObgADC%2B5AAoAAfS4ABAQuQAbAAH0uAAO0DAxEzQ%2BAjMhFSMVMxUjFSEVISIuAjcUHgI7AREjIg4CLjJZe0kBq%2FfQ0AEB%2FkNGd1gylxwzRysXFytHMxwBSVR6TyZ8g3uWfChRfFQ7UTEVAZ4UL07%2F%2FwBJ%2F%2FQCTwM2AiYAGAAAAAcA4QFMAAD%2F%2FwBJ%2F%2FQCTwM2AiYAGAAAAAcA4wFMAAD%2F%2FwBJ%2F%2FQCTwM2AiYAGAAAAAcA5QFMAAD%2F%2FwBJ%2F%2FQCTwNIAiYAGAAAAAcA6gFMAAD%2F%2F%2F%2F4AAACFQM2AiYAHAAAAAcA4wEGAAAAAgAaAAACZQKMABAAIQBZALgAAEVYuAAhLxu5ACEAED5ZuAAARVi4ABwvG7kAHAAEPlm5AAAAAfS4ACEQuQAKAAH0ugAOACEAHBESObgADi%2B5AA0AAfS4AA4QuAAe0LgADRC4AB%2FQMDElMj4CNTQuAisBFTMVIxUTMh4CFRQOAisBESM1NxEBFSpEMBsbMEQqHH19JUt4Vi4uVHVIwExMdxYxUDs6Ty8UoUe2AhUmT3pUVHxRKAEtQgUBGAACAE0AAAI6AowAEAAYADkAuAAARVi4AAAvG7kAAAAQPlm4AABFWLgAEC8buQAQAAQ%2BWbsAGAABAA4ABCu7AAIAAQAXAAQrMDETMxUzMh4CFRQOAisBFSM3MjU0JisBFU2TWzVdRSgpRlw0W5PleD07UgKMYxUxUDs6UzYae%2FBoMynEAP%2F%2FAC%2F%2F9AHZAtQCJgAeAAAABwDgARkAAP%2F%2FAC%2F%2F9AHZAtQCJgAeAAAABwDiARkAAP%2F%2FAC%2F%2F9AHZAtQCJgAeAAAABwDkARkAAP%2F%2FAC%2F%2F9AHZAsYCJgAeAAAABwDmARkAAP%2F%2FAC%2F%2F9AHZAsYCJgAeAAAABwDpARkAAP%2F%2FAC%2F%2F9AHZAvACJgAeAAAABwDrARkAAAADAC%2F%2F9ALtAfwACwA8AEMAmwC4AABFWLgADy8buQAPAAg%2BWbgAAEVYuAAVLxu5ABUACD5ZuAAARVi4AC4vG7kALgAEPlm4AABFWLgAKC8buQAoAAQ%2BWbgALhC5AAMAAfS6ADYALgAPERI5uAA2L7kACQAB9LoAHQAVACgREjm4AB0vuAAoELkAIQAB9LgADxC5ADkAAfS4ABUQuQBAAAH0uAAdELkAQwAB9DAxNxQWMzI2NyYvAQ4BAz4BMzIWFz4BMzIeAhUUBgchHgEzMjY3Fw4BIyImJw4BIyIuAjU0NjcuASMiBgcFNCYjIgYHux8aGi8UCwMBST57MGU2LkYWIEYtMUoxGQQC%2FuIIQi0cMhsyJl0sOFMgNVgyIjYmFYKSAiEqHkAkAfwkKSMxBpUYFxYUHyMVCyoBCR0kKCMkJyVBWDMUIQk0MhQQXxoeKiMqIxcpNh9PWQ8gJxgVMy04MTQA%2F%2F8AJP8jAb4B%2FAImACAAAAAHAO0BGgAA%2F%2F8AJP%2F0AeEC1AImACIAAAAHAOABDgAA%2F%2F8AJP%2F0AeEC1AImACIAAAAHAOIBDgAA%2F%2F8AJP%2F0AeEC1AImACIAAAAHAOQBDgAA%2F%2F8AJP%2F0AeECxgImACIAAAAHAOkBDgAA%2F%2F%2F%2F9QAAANwC1AImAGcAAAAHAOAAigAA%2F%2F8AOAAAAR8C1AImAGcAAAAHAOIAigAA%2F%2F%2F%2F8AAAASQC1AImAGcAAAAHAOQAigAA%2F%2F%2F%2F4QAAATMCxgImAGcAAAAHAOkAigAAAAEAQQAAANQB8AADACUAuAAARVi4AAAvG7kAAAAIPlm4AABFWLgAAi8buQACAAQ%2BWTAxEzMRI0GTkwHw%2FhAAAP%2F%2FAEEAAAIAAsYCJgArAAAABwDmATQAAP%2F%2FACT%2F9AIHAtQCJgAsAAAABwDgARUAAP%2F%2FACT%2F9AIHAtQCJgAsAAAABwDiARUAAP%2F%2FACT%2F9AIHAtQCJgAsAAAABwDkARUAAP%2F%2FACT%2F9AIHAsYCJgAsAAAABwDmARUAAP%2F%2FACT%2F9AIHAsYCJgAsAAAABwDpARUAAAADACT%2F5wIHAgsABwAPACoASQC4AABFWLgAJS8buQAlAAg%2BWbgAAEVYuAAYLxu5ABgABD5ZugAAABgAJRESObkAAgAB9LoACAAlABgREjm4ACUQuQAKAAH0MDE3FjMyNjU0LwEmIyIGFRQXJR4BFRQOAiMiJwcnNy4BNTQ%2BAjMyFhc3F9wXIy02ByQWIi42BwEVGh8oQ1cvSjwrNy8aHyhDWC8jRB0sN3oUSz8pG0MTSz8oG%2BkhVzY%2BYUIjKDUqOiBXNj5hQiMUEzYrAAADACT%2F9AMSAfwACwA0ADsAcQC4AABFWLgAES8buQARAAg%2BWbgAAEVYuAAwLxu5ADAABD5ZuQADAAH0uAARELkACQAB9LgAERC4ABfQuAAwELgAKtC6AB8AFwAqERI5uAAfL7gAKhC5ACMAAfS4ABcQuQA4AAH0uAAfELkAOwAB9DAxNxQWMzI2NTQmIyIGBzQ%2BAjMyFhc%2BATMyHgIVFAYHIR4BMzI2NxcOASMiJicOASMiLgIlNCYjIgYHuSwpKS4uKSkslSZAVS81Tx0eUi0xSjIZBAL%2B4QhCLRwyGzMmXiwtVB8fTzcwVD8kAnAkKSMxBvhBTExBQUxMQT5hQiMuKSotJUFYMxQhCTQyFBBfGh4tKissI0JhbS04MTQAAAABAEH%2F9AJdAscAOABkALgAAEVYuAAFLxu5AAUAEj5ZuAAARVi4ADgvG7kAOAAEPlm4AABFWLgAGy8buQAbAAQ%2BWboADwAbAAUREjm5ACEAAfS6ACQAGwAFERI5ugAwABsABRESObgABRC5ADMAAfQwMRM0PgIzMh4CFRQOAhUUHgQVFA4CIyImJzcWMzI2NTQuBDU0PgI1NCYjIgYVESNBHTpaPDNNMxoYHhgaJy4nGhcvRS0tRiQzMS4aHRonLicaFhsWIR8uLpEB5zBSPCIcLzwgJDEnIRMRFxYaJTUmIjwsGhgVZCQbFRMbFxkhLSEdKiYpGx0nQzr%2BKv%2F%2FADz%2F9AH4AtQCJgAyAAAABwDgAR0AAP%2F%2FADz%2F9AH4AtQCJgAyAAAABwDiAR0AAP%2F%2FADz%2F9AH4AtQCJgAyAAAABwDkAR0AAP%2F%2FADz%2F9AH4AsYCJgAyAAAABwDpAR0AAP%2F%2FAAz%2FPgH9AtQCJgA2AAAABwDiAQoAAP%2F%2FAAz%2FPgH9AsYCJgA2AAAABwDpAQoAAAACACr%2F9AH%2BAuQADgAyAFkAuAAARVi4AC0vG7kALQASPlm4AABFWLgAFy8buQAXAAQ%2BWbsAIQABAAkABCu6ACkAJgADK7gAFxC5AAAAAfS4ACYQuAAP0LgAKRC4AC3cuAApELgAMNAwMSUyNjU8AScuASMiBhUUFhMeARUUDgIjIi4CNTQ%2BAjMyFhcmJwcnNy4BJzceARc3FwEWKjUBFzAaLDc9ijtMIT1YNy9TQCUiOUknHTgVGjuOJnQULRlAI0YhjyZrSU4MFgsZEjo8Oz4B7jyjcDxmSioiPlk3NlQ6HxMXTThHQToOGg1ZEioZSEEAAAAAAgBB%2F0gCFgK9ABYAIQBXALgAAEVYuAAILxu5AAgACD5ZuAAARVi4AAIvG7kAAgASPlm4AABFWLgAAS8buQABAAY%2BWbgAAEVYuAASLxu5ABIABD5ZuQAaAAH0uAAIELkAHwAB9DAxFyMRMxUHPgEzMh4CFRQOAiMiJicXNR4BMzI2NTQjIgfUk5MEGkAiL0s0HCM7SygkNxoEFCgTJjZWKyq4A3WsRhcaJEJdOUBjRSQYF0qzEg5FTYYtAAABABgAAAKvAskALgB8ALgAAEVYuAATLxu5ABMACD5ZuAAARVi4ACsvG7kAKwASPlm4AABFWLgADy8buQAPAAQ%2BWbgAKxC5AAIAAfS4ABMQuAAl0LgAB9C4ABMQuQAQAAH0uAAM0LgACNC4AA8QuAAL0LgAExC4ABLQuAArELgAGdC5ACAAAfQwMQEmIyIGHQEzFSMRIxEjESMRIzU3NTQ%2BAjMyFhcHLgEjIgYdATM1ND4CMzIWFwKVHBcbHllZk6mTQkIVLUczIDYRGwwaFBohqRMsRTIgMhECSwohJh5z%2FoMBff6DAX1tBRcmRDIdCwdtBQYhIxcaJ0Y0HgwGAAAAAQAY%2F%2FQCnwLJAC4AiQC4AABFWLgAEC8buQAQAAg%2BWbgAAEVYuAAWLxu5ABYAEj5ZuAAARVi4AAwvG7kADAAEPlm4AABFWLgAAy8buQADAAQ%2BWbgAEBC5AA0AAfS4AAnQuAAQELgAD9C4AA8vuAAWELkAHAAB9LgAEBC4ACHQuAAl0LgACRC4ACbQuAADELkAKwAB9DAxJQ4BIyIuAj0BIxEjESM1NzU0PgIzMhYXByYjIgYdATM3MxUzFSMVFBYzMjY3Ap8UNiQuQSkTmZNCQhMsRjIfMxEbHBcbHqERend3Ix0MGQoGBgwcMkcryf6DAX1tBRsnRjQeDAZsCiEmHoSEc8cqJQYEAAAAAAMAGf%2F0AokCmAAKABYARACMALgAAEVYuAAvLxu5AC8AED5ZuAAARVi4AB0vG7kAHQAEPlm4AABFWLgAFy8buQAXAAQ%2BWboAOgAaAAMruAAdELkAAwAB9LoABQAaADoREjm6AAgAHQAvERI5uAAIL7gADty4AC8QuQAUAAH0ugAnAAgADhESOboANwAIAA4REjm6AEEAGgA6ERI5MDE3FBYzMjcuAScOARMUFhc%2BATU0JiMiBgEuAScOASMiLgI1ND4CNy4BNTQ%2BAjMyFhUUDgIHHgEXPgE3Mw4BBx4BF6Q1LCgrJkUdFBg9CwojMBYXGSIBhSlUKiplPjVRNxwTISsYExUZLkEpSlYXJzEZHEMkGSYMhhI1Jh85GbclLhofRCQRJgEgEigUFS4hFxwn%2FeQIIhgfIx4zQyYiNywkDyNFHyM%2BLxtQRCA1LSYRID0aIE4uPG0zEBUEAAAAAAIAJf%2F0AesChwATACcANQC4AABFWLgACi8buQAKAA4%2BWbgAAEVYuAAALxu5AAAABD5ZuQAUAAH0uAAKELkAHgAB9DAxBSIuAjU0PgIzMh4CFRQOAicyPgI1NC4CIyIOAhUUHgIBCDNUOyEhO1QzM1Q7ISE7VDMTIBgNDRggExIgGA4OGCAMK1R8UVF6UykpU3pRUXxUK3ITMlRBQVMvEhIvU0FBVDITAAEARgAAAdoCewAMAEMAuAAARVi4AAcvG7kABwAOPlm4AABFWLgADC8buQAMAAQ%2BWbkAAQAB9LgABxC5AAQAAfS5AAIAAfS4AAEQuAAJ0DAxNzMRIzU%2BATczETMVIUaKdzRIImx3%2Fmx3AW9bChwU%2Ffx3AAAAAQAeAAAB5AKHAB8APQC4AABFWLgADy8buQAPAA4%2BWbgAAEVYuAAeLxu5AB4ABD5ZuQAcAAH0uAAZ0LgAANC4AA8QuQAIAAH0MDE3PgM1NCYjIgYHJz4BMzIeAhUUDgIHPgE7ARUhJT1lSSkwKiM2GFAvYkQvTTgeIjlJJxg7F4D%2BQVQ5Y1dLIi8xJxpPMjMdNUouKFNTUygDBXwAAQAW%2F%2FQB3wKHAC8AUwC4AABFWLgAGy8buQAbAA4%2BWbgAAEVYuAAsLxu5ACwABD5ZuQADAAH0ugALABsALBESObgACy%2B5AAwAAfS4ABsQuQAUAAH0ugAkAAwACxESOTAxNx4BMzI2NTQuAiM1Mj4CNTQmIyIGByc%2BATMyHgIVFAYHFR4BFRQOAiMiJidaHUUpLzgPJkI0KzkjDygmIjceSixhOzFROSA3MzdHJkBWME5sI6scJCckFSIXDWgMFx8TISUeGlomKxctQCoyRRYEEE0%2BLEUvGTEpAAAAAAIAEwAAAfgCewAJABQAVwC4AABFWLgAEi8buQASAA4%2BWbgAAEVYuAANLxu5AA0ABD5ZuwAOAAEAAAAEK7gAEhC4AATcuAAAELgACdC4AA4QuAAL0LgACRC4ABDQuAAAELgAE9AwMQE1NDY3Iw4BDwEFIxUjNSE1EzMRMwEmBAIEDBoOVAFYSYn%2B7ei0SQEIZx9RHho2G4pwmJhlAX7%2BjQABABf%2F9AHgAnsAJABHALgAAEVYuAAQLxu5ABAADj5ZuAAARVi4ACEvG7kAIQAEPlm5AAMAAfS6ABcAEAAhERI5uAAXL7gACdy4ABAQuQASAAH0MDE3HgEzMjY1NCYjIg4CBycTIRUjBz4BMzIeAhUUDgIjIiYnWR1DKTE6Ny0OFhUXDkISAWjpCxIhFClJOCAmQFUuTmwmqholMjEwMgMIDAkqAUF8dwgHGDBKMzVSOR4zJgACACn%2F9AHsAocADQAyAEMAuAAARVi4AC8vG7kALwAOPlm4AABFWLgAJS8buQAlAAQ%2BWbsAGwABAAYABCu4ACUQuQAAAAH0uAAvELkAEQAB9DAxJTI2NTQmIyIGBx4DEy4BIyIOAgc%2BAzMyHgIVFA4CIyIuAjU0PgIzMhYXARYhLy8kGDIWBRQaH5ERNhwaLiMWAgwgIiIPKUUzHCI5TSsuV0MoK0ddMj9cHmIxNjMpHCMmMh8NAYESGxQvTDcQGREJGDBKMjJOOB0kTHdUWYJUKS0fAAEALAAAAeYCewAPADMAuAAARVi4AAcvG7kABwAOPlm4AABFWLgAAC8buQAAAAQ%2BWbgABxC5AAUAAfS4AAnQMDEzPgM3ITUhFQ4DByOfBBUnPS3%2B4wG6N0MlEASUT4V5dD58WkN1fY9dAAAAAAMAKv%2F0AegChwANABgAOgBXALgAAEVYuAAlLxu5ACUADj5ZuAAARVi4ADYvG7kANgAEPlm5AAMAAfS6AAsANgAlERI5uAALL7gADtC4ACUQuQATAAH0uAALELgAHNy4AA4QuAAt3DAxNxQWMzI2NTQuAicOATc2NTQmIyIGFRQWBzQ2NzUuATU0PgIzMh4CFRQGBxUeARUUDgIjIi4CqDooJTATJDEeFRyHJykkHSo81D8tJTAeNkssK0gyHDIhLkAgO1MzMFE7IbMqLycoFR4ZFg0UNKItMCUtJCUnLOM6ShgEHEczKEEuGBktQScuRxYEGU4%2FJkAvGxkuQAAAAAACACL%2F9AHmAocACwAwAEMAuAAARVi4ACMvG7kAIwAOPlm4AABFWLgALS8buQAtAAQ%2BWbsAAAABABkABCu4ACMQuQAGAAH0uAAtELkADwAB9DAxEzI2Ny4BIyIGFRQWBx4BMzI%2BAjcOAyMiLgI1ND4CMzIeAhUUDgIjIiYn%2FBgyFQo3IiAwMF8QNhwbLiMVAgwgIiIPKUUyHSI5TSsuV0MpK0ddMj9dHgFWHCNLOTE2Mym%2BEhsUL0s4EBkRCRgwSjIxTzgdJEx3VFmCVCkuHgAAAQA9%2F%2FQA7wCtAAsAGAC4AABFWLgACS8buQAJAAQ%2BWbgAA9wwMTc0NjMyFhUUBiMiJj0zJiYzMyYmM1AoNTUoJzU1AAABAC7%2FPgEAAK0AEAAYALgAAEVYuAAELxu5AAQABD5ZuAAK3DAxFz4BNSMiJjU0NjMyFhUUBgcuNzoIIzY2JjI0XFh0FEAmKygmLklCU3ca%2F%2F8APf%2F0AO8B8QInAIYAAAFEAAYAhgAA%2F%2F8ALv8%2BAQAB8QInAIYAAAFEAAYAhwAAAAIAUf%2F0AQMCngAFABEAGgC4AABFWLgADy8buQAPAAQ%2BWbkACQAB9DAxEyczBwMjBzQ2MzIWFRQGIyImZQWUBRVgKTMmJjMzJiYzAhmFhf7Olyg1NSgnNTUAAAAAAgBR%2F1IBAwH8AAUAEQANALgADy%2B5AAkAAfQwMR8BIzcTMzcUBiMiJjU0NjMyFu8FlAUVYCkzJiYzMyYmMymFhQEylyg1NSgnNTUAAAIAKf%2F0AaUCqgAdACkAKAC4AABFWLgAJy8buQAnAAQ%2BWbsAEQABAAoABCu4ACcQuQAhAAH0MDE3Jj4ENTQmIyIGByc%2BATMyHgIVFA4EFwc0NjMyFhUUBiMiJpoFDxwjIBUlHhwrFFEiXDgqSTUeFiElHxIDmTImJjMzJiYy5yI4MCglIxMfIBoUSikyFCpBLB8xKicqLx6XKDU1KCc1NQACACr%2FRgGmAfwAHQApABsAuAAnL7sACgABABEABCu4ACcQuQAhAAH0MDEBFg4EFRQWMzI2NxcOASMiLgI1ND4EJzcUBiMiJjU0NjMyFgE1BQ8cIyAVJh0cKxRRIlw4Kkk1HhYhJR8TA5kzJiYzMyYmMwEJIjgwKCUjEx8gGhRKKTIUKkEtHzEqJykvHpcoNTUoJzU1AAAAAAEATAFiAOACrgAFAAsAugACAAQAAyswMRMnMw8BI1EFlAUdUAIphYXHAAD%2F%2FwBMAWIBzAKuACYAjgAAAAcAjgDsAAAAAQA3AVIA5QKrABEAKwC4AABFWLgAAy8buQADAAg%2BWbgAAEVYuAAFLxu5AAUACD5ZuQALAAH0MDETDgEVNjMyFhUUBiMiJjU0NjflLS0DByAtLCEvL0ZIAmsXOy0BJyImLEU%2BR2wjAAABAEcBYAD2ArkAEQANALsACwABAAUABCswMRM%2BATUGIyImNTQ2MzIWFRQGB0ctLQMGIC0rIi4wR0gBoBc7LQEnIiYsRD9HbCMAAAD%2F%2FwA3AVIB0QKrACYAkAAAAAcAkADsAAD%2F%2FwBHAWAB4gK5ACYAkQAAAAcAkQDsAAD%2F%2FwBH%2F1gA9gCxAgcAkQAA%2FfgAAP%2F%2FAEf%2FWAHiALEAJwCRAAD9%2BAAHAJEA7P34AAAAAQAxADgA7gHAAAYACwC6AAIABgADKzAxNzU3FwcXBzGGN29vN8hokCyYmCwAAAAAAQA2ADgA8wHAAAYACwC6AAIABQADKzAxNyc3FxUHJ6VvN4aGN%2FyYLJBokCwAAAD%2F%2FwAxADgBqgHAACYAlgAAAAcAlgC8AAD%2F%2FwA2ADgBrwHAACYAlwAAAAcAlwC8AAAAAQArAMkBIQExAAMADQC7AAEAAQACAAQrMDETMxUjK%2Fb2ATFoAAAA%2F%2F8AKwDJASEBMQIGAJoAAAABACsAzgG1ASwAAwANALsAAQABAAIABCswMRMhFSErAYr%2BdgEsXgAAAQArAM4C9QEsAAMADQC7AAEAAQACAAQrMDETIRUhKwLK%2FTYBLF4A%2F%2F8APQDkAO8BnQIHAIYAAADwAAAAAQAoAHsBMQGRABMACwC6AAoAAAADKzAxNyIuAjU0PgIzMh4CFRQOAqwcMCQUFCQwHBwxJBQUJDF7FSYyHh4zJRUVJTMeHjImFQAAAAEADP90Aej%2FxwADAA0AuwAAAAEAAQAEKzAxBRUhNQHo%2FiQ5U1MAAAABAEj%2FTQEoAt8ADgALALoABgAAAAMrMDEXLgE1NDY3Fw4BFRQWFwfMP0VFP1w3MjI3XLNn3oSE3mcmYtZra9ZiJgAAAAEAMP9NARAC3wAOAAsAugAHAA0AAyswMRc%2BATU0Jic3HgEVFAYHJzA4MjI4XD9FRT9cjWLWa2vWYiZn3oSE3mcmAAAAAQBX%2F2gBKgLEAAcAFwC7AAUAAQAGAAQruwABAAEAAgAEKzAxEzMVIxEzFSNX02Vl0wLETv1ATgABAC7%2FaAEBAsQABwAXALsAAAABAAYABCu7AAMAAQACAAQrMDEXESM1MxEjNZRm09NKAsBO%2FKROAAEAH%2F9oASoCxAAzACsAuwAAAAEAAQAEK7sAHgABAB8ABCu7ABAAAQAPAAQrugAqAA8AEBESOTAxBRUjIiY1ND4CNTQuAic1PgM1NC4CNTQ2OwEVIyIGFRQWFRQGBxUeARUUBhUUFjMBKj9CPgMEAwkUIRgYIRQJAwQDPkI%2FEx8WBCQmJiQEFh9KTj1RGyonJxgNGhUNAVYBDRUaDRgnJyobUT1OHSkpTC45MwkECTM5LkwpKR0AAAABAC7%2FaAE5AsQAMwArALsAAAABADIABCu7ABUAAQAUAAQruwAjAAEAJAAEK7oACgAkACMREjkwMRcyNjU0JjU0Njc1LgE1NDY1NCYrATUzMhYVFA4CFRQeAhcVDgMVFB4CFRQGKwE1QR8WBCQmJiQEFh8TP0I%2BAwQDCRQhGBghFAkDBAM%2BQj9KHSkpTC45MwkECTM5LkwpKR1OPVEbKicnGA0aFQ0BVgENFRoNGCcnKhtRPU4AAAAAAQAN%2F2ABNgLGAAMAGAC4AABFWLgAAC8buQAAABI%2BWbgAAtwwMRMzAyPWYMlgAsb8mgAAAAEAVv8GALYC7gADAAsAugABAAIAAyswMRMzESNWYGAC7vwYAAEAHP9gAUYCxgADABgAuAAARVi4AAAvG7kAAAASPlm4AALcMDETMxMjHGDKYALG%2FJoAAAACAFb%2FBgC2Au4AAwAHAAsAugABAAUAAyswMRMzESMXESMRVmBgYGAC7v5KbP46AcYAAAAAAQAmAV0BowLIAA4AFAC4AABFWLgABS8buQAFABI%2BWTAxEzcnNxc3Mxc3FwcXBycHVztsF3QNTQ1zGGw7PVBRAYloMUgYdncZSDFoLFlZAAACACX%2FrAHrArIADQBBACgAuAAARVi4ACgvG7kAKAAEPlm7ACsAAQAlAAQruwA%2BAAEAEQAEKzAxExQeAhc2NTQuAicGNy4BIyIVFB4EFRQGBx4BFRQOAiMiJic3FjMyNjU0LgQ1NDY3LgE1NDYzMhYXoSAyPR0iIDE9HSPaFzgaNig8RzwoKycLDBkwSC83aSJVMTwfHSc6RTonKyUOD11VOlsfAVEYIRsZDxUmGCEbGA8XoRQcJxIbGyAtQC0sQRgQJhciOioYKStLMxgTExwaHi1ALyZDFxApGkRVKRgAAAIAJ%2F%2BwAhwCjAADABAAJQC4AABFWLgAAC8buQAAAA4%2BWbgAAEVYuAAOLxu5AA4ADj5ZMDEBMxEjAyIuAjU0PgI7AREBiZOTYDVeRikoRV00LAKM%2FSQBFR07Vzk%2FVTUW%2FjkAAAADAC3%2F9wLAAo8AEwAnAEUAMwC7ADoAAQBBAAQruwAtAAEANAAEK7gALRC4ACPcuQAFAAH0uABBELgAGdy5AA8AAfQwMRM0PgIzMh4CFRQOAiMiLgI3FB4CMzI%2BAjU0LgIjIg4CFzQ%2BAjMyFhcHLgEjIgYVFBYzMjY3Fw4BIyIuAi01W3hCQnhaNTVaeEJCeFs1RClGYDc3YEYpKUZgNzdgRilRIDVGJi4%2BGDcQIBQwMTIqGSQTMB1AJypHMx0BRU56VS0tVXtNTntXLi5Xe04%2BZEgnJ0hkPj1kRyYmR2Q%2BLEcyGyMYPRERPS0zPBMPRBcdHDNJAAAAAAQAIAE3Aa0CywATACMAMAA5AFcAuAAvL7gAJS%2B4AC8QuAAU3LkAAAAB9LgAJRC4ABrcuQAKAAH0ugAtAC8AJRESObgALS%2B5ADEAAfS6ACoAMQAtERI5uAAvELgALNC4ACUQuQA3AAH0MDETIi4CNTQ%2BAjMyHgIVFA4CJzI2NTQmIyIOAhUUHgInMzIWFRQHFyMnIxUjNzI2NTQmKwEV5ylJNh8fNkkpKUg2Hx82SCk%2FUFA%2FIDQnFRUnNDFZIy4jKTweHTlODw8PDxUBNx42SiwsSjYeHjZKLCxKNh4xU0ZGUxYoOCMjOCgW%2FSAiJRFNPDxkEQsLDzYAAgAx%2F1QDVgKdAEYAUwA%2FALsAOwABAEIABCu7ACcAAQAPAAQruwBJAAEAFgAEK7sAHgABAE8ABCu7AAUAAQAxAAQrugAiAE8AHhESOTAxNzQ%2BAjMyHgIVFA4CIyImJyMOASMiJjU0PgIzMhYXMzczBwYzMj4CNTQuAiMiDgIVFB4CMzI2NxcOASMiLgIlFDMyNj8BJiMiDgIxSHukXFGDXDIqQlEnK0AIAhVEHzxIHzZKKxkmDQIOWy4VQxYrIhQeQmhJQHpgOi9RaTsnUSAgLmA2S4lpPgE%2FNBAfExgPHxcjGQ3Naat6QjRfg05FZ0UiKCQdJVFFLlhFKhcbKtxWGS9DKTZiSyw1YYlUSm5IJBYRUBkXLl6NYkMUGYcdHCoyAAAAAAIAIgAAAfICigAbAB8AoAC4AABFWLgABi8buQAGAAg%2BWbgAAEVYuAAKLxu5AAoACD5ZuAAARVi4AA4vG7kADgAIPlm4AABFWLgAFi8buQAWAAQ%2BWbgAAEVYuAAaLxu5ABoABD5ZuwADAAEAAAAEK7gABhC5AAQAAfS4ABDQuAAR0LgAAxC4ABLQuAAAELgAFNC4AAAQuAAY0LgAAxC4ABzQuAARELgAHdC4AB7QMDE3IzUzNyM1MzczBzM3MwczFSMHMxUjByM3IwcjEzcjB2xKVQ5PWxVUFGgVVBRPWw1UXxdVFmcXVd4OaA27Xm5epaWlpV5uXru7uwEZbm4AAP%2F%2FAEwBuAESAz4CBwC3AAABuAAA%2F%2F8AGwG4AU4DSgIHALgAAAG4AAD%2F%2FwAbAawBUQNKAgcAuQAAAbgAAP%2F%2FACMBuAFwAz4CBwC6AAABuAAAAAIAGv%2F0AV8BkgATAB8AKAC4AAovuAAARVi4AAAvG7kAAAAEPlm5ABQAAfS4AAoQuQAaAAH0MDEXIi4CNTQ%2BAjMyHgIVFA4CJzI2NTQmIyIGFRQWvCM8KxgYKzwjIzwrGRkrPCMZIyMZGSMjDBw2TTExTDUcHDVMMTFNNhxSOEZGNjZGRjgAAAABAEwAAAESAYYACgAiALgACC%2B4AABFWLgACi8buQAKAAQ%2BWbgACBC5AAAAAfQwMRMjNT4DNzMRI6VZFB8aFwxWbQEZQgMICg0J%2FnoAAQAbAAABTgGSABcALAC4AAwvuAAARVi4ABYvG7kAFgAEPlm5ABQAAfS4AADQuAAMELkABgAB9DAxNz4BNTQmIyIGByc2MzIWFRQOAgczFSEtS2AeGxQiET08WUFPER0lFXb%2B3zo%2BXyUdIBkWOFBFPxgtLCwXWgAAAQAb%2F%2FQBUQGSACgAPgC4ABUvuAAARVi4ACUvG7kAJQAEPlm6AAgABwADK7gAJRC5AAIAAfS4ABUQuQAOAAH0ugAdAAcACBESOTAxNxYzMjY1NCM1MjY1NCYjIgYHJz4BMzIeAhUUBgceARUUDgIjIiYnXCMwFyFdJCsaFxMfDj0gQi4bMSYWHhseKRkpNx4zUhp2MBgXMz4XGRQWFhE2IyAPHCkZHysQDC8jGy0fEikmAAAAAgAjAAABcAGGAAUAEABMALgADi%2B4AABFWLgACS8buQAJAAQ%2BWbsAAAABAAoABCu4AA4QuQACAAH0uAAAELgABdC4AAoQuAAH0LgABRC4AAzQuAAAELgAD9AwMTc1NyMPARcjFSM1IzU3MxUz1gYEKCrqOmCzjYY6nSl1TVFGV1c2%2Ben%2F%2FwAdAP4BQAJXAgYAvQAA%2F%2F8AGAD%2BAVwCVwIGAL4AAAACAB0A%2FgFAAlcAGQAiADsAuAAAL7gAEC%2B7AAYAAQAeAAQruAAQELkACQAB9LgAABC4ABXQuAAAELkAGgAB9LoAFwAAABoREjkwMTciJjU0NjcuASMiBgcnPgEzMhYdASMnIw4BNzI2NzUOARUUgi82WGMCFxgULhglIUknQEdTCgQSLgYPFw0yJv46KjU5CRYWEA5EFBhJS70hEhdODgw3BRwSHgAAAAACABgA%2FgFcAlcAEwAfABsAuAAKL7gAAC%2B5ABQAAfS4AAoQuQAaAAH0MDE3Ii4CNTQ%2BAjMyHgIVFA4CJzI2NTQmIyIGFRQWuiA6LRsbLTogIDotGxstOiAdHBwdHRsb%2FhcsQCkpQC0XFy1AKSlALBdSMSkqMTEqKTEAAAAAAgAnAY4BSAKuABMAHwAXALsAFAABAAAABCu7AAoAAQAaAAQrMDETIi4CNTQ%2BAjMyHgIVFA4CJzI2NTQmIyIGFRQWtx41JxYWJzUeHjUnFxcnNR4eJSUeHSYmAY4VJzQgHzUnFRUnNR8gNCcVRikhICkpICEpAAAAAgAUAFMB%2FAI%2FAB4AKgAoALgAAEVYuAALLxu5AAsACj5ZuwAiAAEAGwAEK7gACxC5ACgAAfQwMT8BJjU0NjcnNxc2MzIXNxcHFhUUBgcXBycOASMiJwc3FBYzMjY1NCYjIgYUPSIREDxJRTA2NDJFST0iEhA9SUYXNBo3LkZVMiQkMjIkJDKdPi5BIDcWPkpGGRlGSj4tQCE3Fz5KRwwMGEf3LTU1LS01NQAAAAEAJ%2F%2BSAdQC6QAtAF0AuAAARVi4ACcvG7kAJwAOPlm4AABFWLgAEy8buQATAAQ%2BWbgAJxC5AAMAAfS6AAYAEwAnERI5uAATELgAENC4ABMQuQAaAAH0ugAdACcAExESObgAJxC4ACrQMDEBLgEjIgYVFB4EFRQGBxUjNS4BJzceATMyNjU0LgQ1NDY3NTMVHgEXAYMbMiAkJic7RDsnTk5hLGEjQCZFJConJztEOydRR2EwSB0B3hkZHiIYIR4gLkEwSGcRaGQFJyBjHR0iIxokISEuPi1KYQ5nZQcsHwAAAQAwAAAB8QKHACwAVwC4AABFWLgAFi8buQAWAA4%2BWbgAAEVYuAADLxu5AAMABD5ZuwAkAAEAJQAEK7gAAxC5AAEAAfS4AATQuAAlELgAC9C4ACQQuAAM0LgAFhC5AB0AAfQwMTchFSE1PgE1NCYnIzU3My4BNTQ%2BAjMyFhcHLgEjIgYVFBYXMxUjHgEVFAYH7gED%2Fj8tPQEBZEQGBwkgOU4vOFMiUBEmGCgvBgWUgQEBFRh8fFsUTzYHDQdWBRQoFC9MNR0qJ1AVFS4wEiQSWwcOByQ0GwAAAQAMAAACBAJ7AB0AhAC4AABFWLgAHS8buQAdAA4%2BWbgAAEVYuAAJLxu5AAkADj5ZuAAARVi4ABQvG7kAFAAEPlm6AAQAHQAUERI5ugAZAB0AFBESObgAGS%2B5AAwAAfS4ABkQuAAN0LgAGRC4ABjQuAAYL7gAENC4ABgQuQAVAAH0uAAR0LgAGRC5ABsAAfQwMRMXHgEXMz4BPwEzAzMVIxUzFSMVIzUjNTM1IzUzA6MzDBgOBA0YDTKUk3qampqTmZmZepMCe4UhQiAhQiCF%2FttGN0aTk0Y3RgElAAEAFf%2F0AgcChwAxAG0AuAAARVi4ABcvG7kAFwAOPlm4AABFWLgAAy8buQADAAQ%2BWbsAKQABACoABCu4ACoQuAAH0LgAKRC4AAjQuAApELgAI9y4ABDQuAAjELkAIgAB9LgAEdC4ABcQuQAeAAH0uAADELkALgAB9DAxJQ4BIyImJyM1NyY0NTwBNyM1Nz4DMzIWFwcuASMiBgczFSMVHAEXMxUjHgEzMjY3AgckXDZeiRc%2BNgEBNj4MMEVWMi1WIFITKRorOQzW3wG2rA45Kh0rFFAtL3NwRQQHDAcHDAdEBThXPB8oJk8TGD45ShUIDgdLNzocGgAAAgA8%2F9cB5wKPAAYAJAA3ALsAIQABAAoABCu7ABoAAQAgAAQruAAgELgAANC4ACEQuAAG0LgAChC4AA3QuAAaELgAF9AwMQEOARUUFh8BDgEHFSM1LgM1ND4CNzUzFR4BFwcmJxE%2BATcBEyQkJSPUHUQiUTFPOB8gOk4vUSc%2FFkMeGxUkDwGwEEAtLUEPRRkdBV9fBihBWDY1Vj8pCGFdBB0XWhgD%2FvAEEwwAAAH%2FVf%2F0AQoCmAADABgAuAAARVi4AAAvG7kAAAAEPlm4AAHcMDEHATMBqwFgVf6gDAKk%2FVwAAAD%2F%2F%2F9V%2F%2FQBCgKYAgYAxgAA%2F%2F8AGv%2F0A0ACmAAnALYAAAEGACcAxgF9AAAABwC2AeEAAAAA%2F%2F8AL%2F%2F0AyECmAAnALf%2F4wEGACcAxgFkAAAABwC6AbEAAAAA%2F%2F8AL%2F%2F0AyMCmAAnALf%2F4wEGACcAxgFVAAAABwC4AdUAAAAA%2F%2F8AGf%2F0AykCmAAnALn%2F%2FgEGACcAxgF6AAAABwC6AbkAAAAAAAEAIgBeAe4CNgALAB0AuwADAAEAAAAEK7gAAxC4AAbQuAAAELgACNAwMRMjNTM1MxUzFSMVI9KwsGywsGwBFmi4uGi4AAAAAAEAIgEWAe4BfgADAA0AuwABAAEAAgAEKzAxEyEVISIBzP40AX5oAAABADAAcAHgAiMACwALALoABQAJAAMrMDE%2FASc3FzcXBxcHJwcwjo5Jj49Jjo5Jj4%2B6j5BKkJBKkI9KkJAAAAAAAwAiAEkB7gJLAAsAFwAbADIAuAAARVi4AAYvG7kABgAMPlm7AA8AAQAVAAQruwAZAAEAGgAEK7gABhC5AAAAAfQwMQEiJjU0NjMyFhUUBgM0NjMyFhUUBiMiJgMhFSEBCCMuLiMjLi50LiMjLi4jIy6VAcz%2BNAGxKyIiKysiIiv%2B5SIrKyIiKysBCmgAAAD%2F%2FwAiAKMB7gHyAiYAzQB0AAYAzQCNAAAAAQAiAGgB7gIwAAkAFQC6AAEACAADK7oABQAIAAEREjkwMRMlFQ8BFR8BFSUiAcywh4ew%2FjQBf7F5Oi8ELzp5sQAAAAABACIAaAHuAjAACQAVALoACAABAAMrugAFAAEACBESOTAxAQU1PwE1LwE1BQHu%2FjSwh4ewAcwBGbF5Oi8ELzp5sQAAAAIAIgAAAe4CNgALAA8AOAC4AABFWLgADi8buQAOAAQ%2BWbsAAwABAAAABCu4AAMQuAAG0LgAABC4AAjQuAAOELkADAAB9DAxEyM1MzUzFTMVIxUjByEVIdKwsGywsGywAcz%2BNAEmaKioaIg2aAAAAAABADIBEgHeAp4ACQA4ALgAAEVYuAAALxu5AAAAED5ZuAAC3LgAABC5AAUAAfS6AAYAAAACERI5uAACELgACNC4AAnQMDETMxMjLwEjDwEjzXabeS4tBC0ueQKe%2FnSAh4eAAAAAAQAdAPAB8wGkABcAJwC7AAgAAQAPAAQruAAPELgAFNy5AAMAAfS4AAvQuAAPELgAF9AwMRM%2BATMyHgIzMjY3Fw4BIyIuAiMiBgcdJUsmHy4nIxUVIxBMJUsmHy4nIxUUJBABPTkuGBwYHhw7OS4YHBgeHAABACIAXgHuAX4ABQANALsAAQABAAQABCswMRMhESM1ISIBzGz%2BoAF%2B%2FuC4AAEAQf84Af0B8AAXAD8AuAAARVi4AAsvG7kACwAEPlm4AABFWLgAES8buQARAAQ%2BWbkABQAB9LoADgARAAUREjm6ABQAEQAFERI5MDETMxEUFjMyNjcRMxEjJyMOASMiJicXFSNBkx0gHCcWk3gLBBM5IRIfDQmTAfD%2B3zYoGh0BSP4QRSQlCA5agAD%2F%2FwCBAj4BaALUAAcA4AEWAAAAAP%2F%2FAMQCPgGrAtQABwDiARYAAAAA%2F%2F8AfAI%2BAbAC1AAHAOQBFgAAAAD%2F%2FwBwAj8BvALGAAcA5gEWAAAAAP%2F%2FAG0COwG%2FAsYABwDpARYAAAAA%2F%2F8AjAJQAaACrQAHAOgBFgAAAAD%2F%2FwClAiQBhwLwAAcA6wEWAAAAAP%2F%2FALD%2FIwFsAAQABwDtARcAAAAAAAH%2FawI%2BAFIC1AADABgAuAAARVi4AAMvG7kAAwAMPlm4AAHcMDEDMxcjlY5ZawLUlgAAAAAB%2F14CvgBLAzYAAwALALoAAQADAAMrMDEDMxcjopxRdQM2eAAB%2F64CPgCVAtQAAwAYALgAAEVYuAAALxu5AAAADD5ZuAAC3DAxEyM3MxlrWY4CPpYAAAAAAf%2B1Ar4AogM2AAMACwC6AAIAAAADKzAxEyM3Myp1UZwCvngAAf9mAj4AmgLUAAcANwC4AABFWLgABi8buQAGAAw%2BWbgAAEVYuAADLxu5AAMADD5ZuAAGELgAANy6AAUAAAAGERI5MDEDMxcjJyMHIzp0YGM1BDVjAtSWT08AAAAB%2F1gCvgCoAzYABwAXALoABgABAAMruAAGELgAB9y4AAPQMDEDNzMXIycjB6hgkGBxNQQ1Ar54eD09AAAB%2F1oCPwCmAsYAGABBALgAAEVYuAAALxu5AAAADD5ZuAAARVi4AA8vG7kADwAMPlm7ABQAAQADAAQruAAPELkACAAB9LgAAxC4AAzQMDEDPgEzMh4CMzI2NzMOASMiLgIjIgYHI6YIOCgUIhwZCw0QBksIOCgUIR0ZCw0QBksCP0VCDhAOFBhEQw4RDhQZAAAB%2F1YCvwCqA0cAFwAjALoAAAARAAMrugAFAAwAAyu4AAAQuAAI0LgADBC4ABTQMDETIi4CIyIGByM%2BATMyHgIzMjY3Mw4BPxQiHRkMDRQFSwg8JxQiHRkMDRQFSwg8Ar8OEQ4UGUVDDhEOFBlFQwAAAf92AlAAigKtAAMACwC4AAMvuAAB3DAxAyEVIYoBFP7sAq1dAAAAAAL%2FVwI7AKkCxgALABcAKAC4AABFWLgAAC8buQAAAAw%2BWbgABty4AAAQuAAM0LgABhC4ABLQMDEDIiY1NDYzMhYVFAYzIiY1NDYzMhYVFAZjHycnHx8nJ6cfJycfHycnAjsoHh0oKB0eKCgeHSgoHR4oAAAAAv9XArwAqQNIAAsAFwAbALoABgAAAAMruAAAELgADNC4AAYQuAAS0DAxAyImNTQ2MzIWFRQGMyImNTQ2MzIWFRQGYx8nJx8fJyenHycnHx8nJwK8KB4eKCgeHigoHh4oKB4eKAAAAAAC%2F48CJABxAvAACwAXABMAugAMAAAAAyu6AAYAEgADKzAxESImNTQ2MzIWFRQGJzI2NTQmIyIGFRQWMz4%2BMzM%2BPjMRGBgRERgYAiQ3Ly83Ny8vNzcaFRUaGhUVGgAAAAAC%2F48CuQBxA30ACwAXABMAugAMAAAAAyu6AAYAEgADKzAxESImNTQ2MzIWFRQGJzI2NTQmIyIGFRQWMz4%2BMzM%2BPjMRFxcRERgYArk1LS01NS0tNTcXFBQXFxQUFwAAAAAB%2F5n%2FIwBVAAQAEQATALoAAgARAAMrugALAAoAAyswMSczBx4BFRQOAgcnPgE1NCYnJ1YUGiAdMEAkCyQvGSIELwgjHxolGg0COwQUFA0VCAAAAAAB%2F5n%2FIwBVAAQAEQATALoAEQACAAMrugAKAAsAAyswMSczBx4BFRQOAgcnPgE1NCYnJ1YUGiAdMEAkCyQvGSIELwgjHxolGg0COwQUFA0VCAAAAP%2F%2FADUAAADfAtICBgAmAAAAAQAAAPEAZAAHAGkABQABAAAAAAAKAAACAAFzAAMAAQAAAGAAYABgAGAArgEWAWQBqAHmAhwCeAKyAtIDBgNOA3QD2gQqBHQEugUgBXIF3gYKBkYGggb4B1QHkgfICD4Isgj8CXQJ0gokCsYLFAtIC5IL2AwMDIwM4g0sDaAOFg5gDsoPFA9qD6YQGBByEMYQ%2BhEGERIRHhEqETYRQhGoEbQRwBHMEdgR5BHwEfwSCBIUEiASLBI4EkQSUBJcEuITPhNKE1YTYhNuE3oT2BQaFCYUMhQ%2BFEoUVhRiFRQVIBUsFTgVRBVQFVwVaBV0FYAVoBWsFbgVxBXQFdwV6BZOFt4XXBdoF3QXgBeMF5gXpBgeGHwY%2FBmCGjAahBq%2BGwwbehvKHCQcjhzEHUQdrB3OHfYeAh4OHjweYh6yHv4fFB8gH1Qfeh%2BGH5IfnB%2BqH8If2h%2FmH%2FIgBiAOICIgNiBAIGYgeiCcIL4g2iD2IVIhriHIIdoh9CIOIjYipiLYI1Qj0CRiJOIk7CT2JQAlCiVOJXQlsCYKJkwmVCZcJrAm7icqJ4An8ChcKMopRimcKbgpwCnSKeQp9ioIKiwqQCpgKqgqtCrWKvgrMCtiK5wrsiv4LAIsDCwWLCAsKiw0LD4sSCxiLHQsjiygLM4s7C00LWwtgC26Le4uHi5OLnguoi6iLqoAAQAAAAEMzGhqkv5fDzz1AAkD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j1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Ffont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v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U0NjsBEQEhArz6FR384B0V%2Bv4MBLACiv3aHRUyMhUdAiYCJgAAAAEADgAIBEwEnAAfAAABJTYWFREUBgcGLgE2NzYXEQURFAYHBi4BNjc2FxE0NgFwAoUnMFNGT4gkV09IQv2oWEFPiCRXT0hCHQP5ow8eIvzBN1EXGSltchkYEAIJm%2F2iKmAVGilucRoYEQJ%2FJioAAAACAAn%2F%2BAS7BKcAHQApAAAAMh4CFQcXFAcBFgYPAQYiJwEGIycHIi4CND4BBCIOARQeATI%2BATQmAZDItoNOAQFOARMXARY7GikT%2Fu13jgUCZLaDTk6DAXKwlFZWlLCUVlYEp06DtmQCBY15%2Fu4aJRg6FBQBEk0BAU6Dtsi2g1tWlLCUVlaUsJQAAQBkAFgErwREABkAAAE%2BAh4CFRQOAwcuBDU0PgIeAQKJMHt4dVg2Q3mEqD4%2Bp4V4Qzhadnh5A7VESAUtU3ZAOXmAf7JVVbJ%2FgHk5QHZTLQVIAAAAAf%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAAgAAgAAAAAAAAABAAAFFP%2BFAAAFFP9R%2FtQF3AABAAAAAAAAAAAAAAAAAAAAowG4ACgAAAAAAZAAAASwAAAEsABkBLAAAASwAAAEsABwAooAAAUUAAACigAABRQAAAGxAAABRQAAANgAAADYAAAAogAAAQQAAABIAAABBAAAAUUAAASwAGQEsAB7BLAAyASwAMgB9AAABLD%2F8gSwAAAEsAAABLD%2F8ASwAAAEsAAOBLAACQSwAGQEsP%2FTBLD%2F0wSwAAAEsAAABLAAAASwAAAEsAAABLAAJgSwAG4EsAAXBLAAFwSwABcEsABkBLAAGgSwAGQEsAAMBLAAZASwABcEsP%2BcBLAAZASwABcEsAAXBLAAAASwABcEsAAXBLAAFwSwAGQEsAAABLAAZASwAAAEsAAABLAAAASwAAAEsAAABLAAAASwAAAEsAAABLAAZASwAMgEsAAABLAAAASwADUEsABkBLAAyASw%2F7UEsAAhBLAAAASwAAAEsAAABLAAAASwAAAEsP%2BcBLAAAASwAAAEsAAABLAA2wSwABcEsAB1BLAAAASwAAAEsAAABLAACgSwAMgEsAAABLAAnQSwAMgEsADIBLAAyASwAAAEsP%2F%2BBLABLASwAGQEsACIBLABOwSwABcEsAAXBLAAFwSwABcEsAAXBLAAFwSwAAAEsAAXBLAAFwSwABcEsAAXBLAAAASwALcEsAC3BLAAAASwAAAEsABJBLAAFwSwAAAEsAAABLAAXQSw%2F9wEsP%2FcBLD%2FnwSwAGQEsAAABLAAAASwAAAEsABkBLD%2F%2FwSwAAAEsP9RBLAABgSwAAAEsAAABLABRQSwAAEEsAAABLD%2FnASwAEoEsAAUBLAAAASwAAAEsAAABLD%2FnASwAGEEsP%2F9BLAAFgSwABYEsAAWBLAAFgSwABgEsAAABMQAAASwAGQAAAAAAAD%2F2ABkADkAyAAAAScAZAAZABkAGQAZABkAGQAZAAAAAAAAAAAAAADZAAAAAAAOAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAMAZABkAAAAEAAAAAAAZP%2Bc%2F5z%2FnP%2Bc%2F5z%2FnP%2Bc%2F5wACQAJ%2F%2FL%2F8gBkAHkAJwBkAGQAAAAAAGT%2FogAAAAAAAAAAAAAAAADIAGQAAAABAI8AAP%2Bc%2F5wAZAAEAMgAyAAAAGQBkABkAAAAZAEs%2F7UAAAAAAAAAAAAAAAAAAABkAAABLAFBADIAMgAIAAAAAADIAT4AZgBmANkAyADIAAAAKgAqACoAKgCyAOgA6AFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BpAIGAiICfgKGAqwC5ANGA24DjAPEBAgEMgRiBKIE3AVcBboGcgb0ByAHYgfKCB4IYgi%2BCTYJhAm2Cd4KKApMCpQK4gswC4oLygwIDFgNKg1eDbAODg5oDrQPKA%2BmD%2BYQEhBUEJAQqhEqEXYRthIKEjgSfBLAExoTdBPQFCoU1BU8FagVzBYEFjYWYBawFv4XUhemGAIYLhhqGJYYsBjgGP4ZKBloGZQZxBnaGe4aNhpoGrga9hteG7QcMhyUHOIdHB1EHWwdlB28HeYeLh52HsAfYh%2FSIEYgviEyIXYhuCJAIpYiuCMOIyIjOCN6I8Ij4CQCJDAkXiSWJOIlNCVgJbwmFCZ%2BJuYnUCe8J%2FgoNChwKKwpoCnMKiYqSiqEKworeiwILGgsuizsLRwtiC30LiguZi6iLtgvDi9GL34vsi%2F4MD4whDDSMRIxYDGuMegyJDJeMpoy3jMiMz4zaDO2NBg0YDSoNNI1LDWeNeg2PjZ8Ntw3GjdON5I31DgQOEI4hjjIOQo5SjmIOcw6HDpsOpo63jugO9w8GDxQPKI8%2BD0yPew%2BOj6MPtQ%2FKD9uP6o%2F%2BkBIQIBAxkECQX5CGEKoQu5DGENCQ3ZDoEPKRBBEYESuRPZFWkW2RgZGdEa0RvZHNkd2R7ZH9kgWSDJITkhqSIZIzEkSSThJXkmESapKAkouSlIAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAAAAABAAxgABAAAAAAATABIAAAADAAEECQAAAGoAEgADAAEECQABACgAfAADAAEECQACAA4ApAADAAEECQADAEwAsgADAAEECQAEADgA%2FgADAAEECQAFAHgBNgADAAEECQAGADYBrgADAAEECQAIABYB5AADAAEECQAJABYB%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%2FtQAyAAAAAAAAAAAAAAAAAAAAAAAAAAABFwAAAQIBAwADAA0ADgEEAJYBBQEGAQcBCAEJAQoBCwEMAQ0BDgEPARABEQESARMA7wEUARUBFgEXARgBGQEaARsBHAEdAR4BHwEgASEBIgEjASQBJQEmAScBKAEpASoBKwEsAS0BLgEvATABMQEyATMBNAE1ATYBNwE4ATkBOgE7ATwBPQE%2BAT8BQAFBAUIBQwFEAUUBRgFHAUgBSQFKAUsBTAFNAU4BTwFQAVEBUgFTAVQBVQFWAVcBWAFZAVoBWwFcAV0BXgFfAWABYQFiAWMBZAFlAWYBZwFoAWkBagFrAWwBbQFuAW8BcAFxAXIBcwF0AXUBdgF3AXgBeQF6AXsBfAF9AX4BfwGAAYEBggGDAYQBhQGGAYcBiAGJAYoBiwGMAY0BjgGPAZABkQGSAZMBlAGVAZYBlwGYAZkBmgGbAZwBnQGeAZ8BoAGhAaIBowGkAaUBpgGnAagBqQGqAasBrAGtAa4BrwGwAbEBsgGzAbQBtQG2AbcBuAG5AboBuwG8Ab0BvgG%2FAcABwQHCAcMBxAHFAcYBxwHIAckBygHLAcwBzQHOAc8B0AHRAdIB0wHUAdUB1gHXAdgB2QHaAdsB3AHdAd4B3wHgAeEB4gHjAeQB5QHmAecB6AHpAeoB6wHsAe0B7gHvAfAB8QHyAfMB9AH1AfYB9wH4AfkB%2BgH7AfwB%2FQH%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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDIyOyIgZD0iTTYwMCAxMjAwdi00MDBxMCAtNDEgMjkuNSAtNzAuNXQ3MC41IC0yOS41aDMwMHYtNjUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC04MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djExMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDQ1MHpNMTAwMCA4MDBoLTI1MHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MjUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMjM7IiBkPSJNNjAwIDExNzdxMTE3IDAgMjI0IC00NS41dDE4NC41IC0xMjN0MTIzIC0xODQuNXQ0NS41IC0yMjR0LTQ1LjUgLTIyNHQtMTIzIC0xODQuNXQtMTg0LjUgLTEyM3QtMjI0IC00NS41dC0yMjQgNDUuNXQtMTg0LjUgMTIzdC0xMjMgMTg0LjV0LTQ1LjUgMjI0dDQ1LjUgMjI0dDEyMyAxODQuNXQxODQuNSAxMjN0MjI0IDQ1LjV6TTYwMCAxMDI3cS0xMTYgMCAtMjE0LjUgLTU3dC0xNTUuNSAtMTU1LjV0LTU3IC0yMTQuNXQ1NyAtMjE0LjUgdDE1NS41IC0xNTUuNXQyMTQuNSAtNTd0MjE0LjUgNTd0MTU1LjUgMTU1LjV0NTcgMjE0LjV0LTU3IDIxNC41dC0xNTUuNSAxNTUuNXQtMjE0LjUgNTd6TTUyNSA5MDBoNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0yNzVoMTc1cTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtNTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDM1OyIgZD0iTTYwMCAxMTc1cTExNiAwIDIyNyAtNDkuNXQxOTIuNSAtMTMxdDEzMSAtMTkyLjV0NDkuNSAtMjI3di0zMDBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC01MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djMwMHEwIDEyNyAtNzAuNSAyMzEuNXQtMTg0LjUgMTYxLjV0LTI0NSA1N3QtMjQ1IC01N3QtMTg0LjUgLTE2MS41dC03MC41IC0yMzEuNXYtMzAwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtNTAgcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MzAwcTAgMTE2IDQ5LjUgMjI3dDEzMSAxOTIuNXQxOTIuNSAxMzF0MjI3IDQ5LjV6TTIyMCA1MDBoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR2NDYwcTAgOCA2IDE0dDE0IDZ6TTgyMCA1MDBoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR2NDYwIHEwIDggNiAxNHQxNCA2eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzY7IiBkPSJNMzIxIDgxNGwyNTggMTcycTkgNiAxNSAyLjV0NiAtMTMuNXYtNzUwcTAgLTEwIC02IC0xMy41dC0xNSAyLjVsLTI1OCAxNzJxLTIxIDE0IC00NiAxNGgtMjUwcS0xMCAwIC0xNy41IDcuNXQtNy41IDE3LjV2MzUwcTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoMjUwcTI1IDAgNDYgMTR6TTkwMCA2NjhsMTIwIDEyMHE3IDcgMTcgN3QxNyAtN2wzNCAtMzRxNyAtNyA3IC0xN3QtNyAtMTdsLTEyMCAtMTIwbDEyMCAtMTIwcTcgLTcgNyAtMTcgdC03IC0xN2wtMzQgLTM0cS03IC03IC0xNyAtN3QtMTcgN2wtMTIwIDExOWwtMTIwIC0xMTlxLTcgLTcgLTE3IC03dC0xNyA3bC0zNCAzNHEtNyA3IC03IDE3dDcgMTdsMTE5IDEyMGwtMTE5IDEyMHEtNyA3IC03IDE3dDcgMTdsMzQgMzRxNyA4IDE3IDh0MTcgLTh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNzsiIGQ9Ik0zMjEgODE0bDI1OCAxNzJxOSA2IDE1IDIuNXQ2IC0xMy41di03NTBxMCAtMTAgLTYgLTEzLjV0LTE1IDIuNWwtMjU4IDE3MnEtMjEgMTQgLTQ2IDE0aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNTBxMjUgMCA0NiAxNHpNNzY2IDkwMGg0cTEwIC0xIDE2IC0xMHE5NiAtMTI5IDk2IC0yOTBxMCAtMTU0IC05MCAtMjgxcS02IC05IC0xNyAtMTBsLTMgLTFxLTkgMCAtMTYgNiBsLTI5IDIzcS03IDcgLTguNSAxNi41dDQuNSAxNy41cTcyIDEwMyA3MiAyMjlxMCAxMzIgLTc4IDIzOHEtNiA4IC00LjUgMTh0OS41IDE3bDI5IDIycTcgNSAxNSA1eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzg7IiBkPSJNOTY3IDEwMDRoM3ExMSAtMSAxNyAtMTBxMTM1IC0xNzkgMTM1IC0zOTZxMCAtMTA1IC0zNCAtMjA2LjV0LTk4IC0xODUuNXEtNyAtOSAtMTcgLTEwaC0zcS05IDAgLTE2IDZsLTQyIDM0cS04IDYgLTkgMTZ0NSAxOHExMTEgMTUwIDExMSAzMjhxMCA5MCAtMjkuNSAxNzZ0LTg0LjUgMTU3cS02IDkgLTUgMTl0MTAgMTZsNDIgMzNxNyA1IDE1IDV6TTMyMSA4MTRsMjU4IDE3MnE5IDYgMTUgMi41dDYgLTEzLjV2LTc1MHEwIC0xMCAtNiAtMTMuNSB0LTE1IDIuNWwtMjU4IDE3MnEtMjEgMTQgLTQ2IDE0aC0yNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYzNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgyNTBxMjUgMCA0NiAxNHpNNzY2IDkwMGg0cTEwIC0xIDE2IC0xMHE5NiAtMTI5IDk2IC0yOTBxMCAtMTU0IC05MCAtMjgxcS02IC05IC0xNyAtMTBsLTMgLTFxLTkgMCAtMTYgNmwtMjkgMjNxLTcgNyAtOC41IDE2LjV0NC41IDE3LjVxNzIgMTAzIDcyIDIyOXEwIDEzMiAtNzggMjM4IHEtNiA4IC00LjUgMTguNXQ5LjUgMTYuNWwyOSAyMnE3IDUgMTUgNXoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY2OyIgZD0iTTM1MCAxMTAwaDM2MXEtMTY0IC0xNDYgLTIxNiAtMjAwaC0xOTVxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNWwyMDAgMTUzdi0xMDNxMCAtMTY1IC05Mi41IC0yNTcuNXQtMjU3LjUgLTkyLjVoLTQwMHEtMTY1IDAgLTI1Ny41IDkyLjV0LTkyLjUgMjU3LjV2NDAwcTAgMTY1IDkyLjUgMjU3LjV0MjU3LjUgOTIuNXogTTgyNCAxMDczbDMzOSAtMzAxcTggLTcgOCAtMTcuNXQtOCAtMTcuNWwtMzQwIC0zMDZxLTcgLTYgLTEyLjUgLTR0LTYuNSAxMXYyMDNxLTI2IDEgLTU0LjUgMHQtNzguNSAtNy41dC05MiAtMTcuNXQtODYgLTM1dC03MCAtNTdxMTAgNTkgMzMgMTA4dDUxLjUgODEuNXQ2NSA1OC41dDY4LjUgNDAuNXQ2NyAyNC41dDU2IDEzLjV0NDAgNC41djIxMHExIDEwIDYuNSAxMi41dDEzLjUgLTQuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDg2OyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02NzUgMTAwMGgtMTUwcS0xMCAwIC0xNy41IC03LjV0LTcuNSAtMTcuNXYtMTUwcTAgLTEwIDcuNSAtMTcuNSB0MTcuNSAtNy41aDE1MHExMCAwIDE3LjUgNy41dDcuNSAxNy41djE1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjV6TTY3NSA3MDBoLTI1MHEtMTAgMCAtMTcuNSAtNy41dC03LjUgLTE3LjV2LTUwcTAgLTEwIDcuNSAtMTcuNXQxNy41IC03LjVoNzV2LTIwMGgtNzVxLTEwIDAgLTE3LjUgLTcuNXQtNy41IC0xNy41di01MHEwIC0xMCA3LjUgLTE3LjV0MTcuNSAtNy41aDM1MHExMCAwIDE3LjUgNy41dDcuNSAxNy41djUwcTAgMTAgLTcuNSAxNy41IHQtMTcuNSA3LjVoLTc1djI3NXEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4NzsiIGQ9Ik01MjUgMTIwMGgxNTBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di0xOTRxMTAzIC0yNyAxNzguNSAtMTAyLjV0MTAyLjUgLTE3OC41aDE5NHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE5NHEtMjcgLTEwMyAtMTAyLjUgLTE3OC41dC0xNzguNSAtMTAyLjV2LTE5NHEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE5NCBxLTEwMyAyNyAtMTc4LjUgMTAyLjV0LTEwMi41IDE3OC41aC0xOTRxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxNTBxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWgxOTRxMjcgMTAzIDEwMi41IDE3OC41dDE3OC41IDEwMi41djE5NHEwIDEwIDcuNSAxNy41dDE3LjUgNy41ek03MDAgODkzdi0xNjhxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC0xNTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXYxNjhxLTY4IC0yMyAtMTE5IC03NCB0LTc0IC0xMTloMTY4cTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTUwcTAgLTEwIC03LjUgLTE3LjV0LTE3LjUgLTcuNWgtMTY4cTIzIC02OCA3NCAtMTE5dDExOSAtNzR2MTY4cTAgMTAgNy41IDE3LjV0MTcuNSA3LjVoMTUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMTY4cTY4IDIzIDExOSA3NHQ3NCAxMTloLTE2OHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djE1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDE2OCBxLTIzIDY4IC03NCAxMTl0LTExOSA3NHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkwOyIgZD0iTTYwMCAxMTc3cTExNyAwIDIyNCAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI0dC00NS41IC0yMjR0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNCAtNDUuNXQtMjI0IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNHQ0NS41IDIyNHQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNCA0NS41ek02MDAgMTAyN3EtMTE2IDAgLTIxNC41IC01N3QtMTU1LjUgLTE1NS41dC01NyAtMjE0LjVxMCAtMTIwIDY1IC0yMjUgbDU4NyA1ODdxLTEwNSA2NSAtMjI1IDY1ek05NjUgODE5bC01ODQgLTU4NHExMDQgLTYyIDIxOSAtNjJxMTE2IDAgMjE0LjUgNTd0MTU1LjUgMTU1LjV0NTcgMjE0LjVxMCAxMTUgLTYyIDIxOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTM5IDU4Mmw1MjIgNDI3cTE2IDEzIDI3LjUgOHQxMS41IC0yNnYtMjkxaDU1MHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMjAwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC01NTB2LTI5MXEwIC0yMSAtMTEuNSAtMjZ0LTI3LjUgOGwtNTIyIDQyN3EtMTYgMTMgLTE2IDMydDE2IDMyeiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwOTI7IiBkPSJNNjM5IDEwMDlsNTIyIC00MjdxMTYgLTEzIDE2IC0zMnQtMTYgLTMybC01MjIgLTQyN3EtMTYgLTEzIC0yNy41IC04dC0xMS41IDI2djI5MWgtNTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYyMDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41aDU1MHYyOTFxMCAyMSAxMS41IDI2dDI3LjUgLTh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik02ODIgMTE2MWw0MjcgLTUyMnExMyAtMTYgOCAtMjcuNXQtMjYgLTExLjVoLTI5MXYtNTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0yMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djU1MGgtMjkxcS0yMSAwIC0yNiAxMS41dDggMjcuNWw0MjcgNTIycTEzIDE2IDMyIDE2dDMyIC0xNnoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTAzOyIgZD0iTTExNjUgMTE5MHE4IDMgMjEgLTYuNXQxMyAtMTcuNXEtMiAtMTc4IC0yNC41IC0zMjMuNXQtNTUuNSAtMjQ1LjV0LTg3IC0xNzQuNXQtMTAyLjUgLTExOC41dC0xMTggLTY4LjV0LTExOC41IC0zM3QtMTIwIC00LjV0LTEwNSA5LjV0LTkwIDE2LjVxLTYxIDEyIC03OCAxMXEtNCAxIC0xMi41IDB0LTM0IC0xNC41dC01Mi41IC00MC41bC0xNTMgLTE1M3EtMjYgLTI0IC0zNyAtMTQuNXQtMTEgNDMuNXEwIDY0IDQyIDEwMnE4IDggNTAuNSA0NSB0NjYuNSA1OHExOSAxNyAzNSA0N3QxMyA2MXEtOSA1NSAtMTAgMTAyLjV0NyAxMTF0MzcgMTMwdDc4IDEyOS41cTM5IDUxIDgwIDg4dDg5LjUgNjMuNXQ5NC41IDQ1dDExMy41IDM2dDEyOSAzMXQxNTcuNSAzN3QxODIgNDcuNXpNMTExNiAxMDk4cS04IDkgLTIyLjUgLTN0LTQ1LjUgLTUwcS0zOCAtNDcgLTExOSAtMTAzLjV0LTE0MiAtODkuNWwtNjIgLTMzcS01NiAtMzAgLTEwMiAtNTd0LTEwNCAtNjh0LTEwMi41IC04MC41dC04NS41IC05MSB0LTY0IC0xMDQuNXEtMjQgLTU2IC0zMSAtODZ0MiAtMzJ0MzEuNSAxNy41dDU1LjUgNTkuNXEyNSAzMCA5NCA3NS41dDEyNS41IDc3LjV0MTQ3LjUgODFxNzAgMzcgMTE4LjUgNjl0MTAyIDc5LjV0OTkgMTExdDg2LjUgMTQ4LjVxMjIgNTAgMjQgNjB0LTYgMTl6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEwNDsiIGQ9Ik02NTMgMTIzMXEtMzkgLTY3IC01NC41IC0xMzF0LTEwLjUgLTExNC41dDI0LjUgLTk2LjV0NDcuNSAtODB0NjMuNSAtNjIuNXQ2OC41IC00Ni41dDY1IC0zMHEtNCA3IC0xNy41IDM1dC0xOC41IDM5LjV0LTE3IDM5LjV0LTE3IDQzdC0xMyA0MnQtOS41IDQ0LjV0LTIgNDJ0NCA0M3QxMy41IDM5dDIzIDM4LjVxOTYgLTQyIDE2NSAtMTA3LjV0MTA1IC0xMzh0NTIgLTE1NnQxMyAtMTU5dC0xOSAtMTQ5LjVxLTEzIC01NSAtNDQgLTEwNi41IHQtNjggLTg3dC03OC41IC02NC41dC03Mi41IC00NXQtNTMgLTIycS03MiAtMjIgLTEyNyAtMTFxLTMxIDYgLTEzIDE5cTYgMyAxNyA3cTEzIDUgMzIuNSAyMXQ0MSA0NHQzOC41IDYzLjV0MjEuNSA4MS41dC02LjUgOTQuNXQtNTAgMTA3dC0xMDQgMTE1LjVxMTAgLTEwNCAtMC41IC0xODl0LTM3IC0xNDAuNXQtNjUgLTkzdC04NCAtNTJ0LTkzLjUgLTExdC05NSAyNC41cS04MCAzNiAtMTMxLjUgMTE0dC01My41IDE3MXEtMiAyMyAwIDQ5LjUgdDQuNSA1Mi41dDEzLjUgNTZ0MjcuNSA2MHQ0NiA2NC41dDY5LjUgNjguNXEtOCAtNTMgLTUgLTEwMi41dDE3LjUgLTkwdDM0IC02OC41dDQ0LjUgLTM5dDQ5IC0ycTMxIDEzIDM4LjUgMzZ0LTQuNSA1NXQtMjkgNjQuNXQtMzYgNzV0LTI2IDc1LjVxLTE1IDg1IDIgMTYxLjV0NTMuNSAxMjguNXQ4NS41IDkyLjV0OTMuNSA2MXQ4MS41IDI1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEwNTsiIGQ9Ik02MDAgMTA5NHE4MiAwIDE2MC41IC0yMi41dDE0MCAtNTl0MTE2LjUgLTgyLjV0OTQuNSAtOTV0NjggLTk1dDQyLjUgLTgyLjV0MTQgLTU3LjV0LTE0IC01Ny41dC00MyAtODIuNXQtNjguNSAtOTV0LTk0LjUgLTk1dC0xMTYuNSAtODIuNXQtMTQwIC01OXQtMTU5LjUgLTIyLjV0LTE1OS41IDIyLjV0LTE0MCA1OXQtMTE2LjUgODIuNXQtOTQuNSA5NXQtNjguNSA5NXQtNDMgODIuNXQtMTQgNTcuNXQxNCA1Ny41dDQyLjUgODIuNXQ2OCA5NSB0OTQuNSA5NXQxMTYuNSA4Mi41dDE0MCA1OXQxNjAuNSAyMi41ek04ODggODI5cS0xNSAxNSAtMTggMTJ0NSAtMjJxMjUgLTU3IDI1IC0xMTlxMCAtMTI0IC04OCAtMjEydC0yMTIgLTg4dC0yMTIgODh0LTg4IDIxMnEwIDU5IDIzIDExNHE4IDE5IDQuNSAyMnQtMTcuNSAtMTJxLTcwIC02OSAtMTYwIC0xODRxLTEzIC0xNiAtMTUgLTQwLjV0OSAtNDIuNXEyMiAtMzYgNDcgLTcxdDcwIC04MnQ5Mi41IC04MXQxMTMgLTU4LjV0MTMzLjUgLTI0LjUgdDEzMy41IDI0dDExMyA1OC41dDkyLjUgODEuNXQ3MCA4MS41dDQ3IDcwLjVxMTEgMTggOSA0Mi41dC0xNCA0MS41cS05MCAxMTcgLTE2MyAxODl6TTQ0OCA3MjdsLTM1IC0zNnEtMTUgLTE1IC0xOS41IC0zOC41dDQuNSAtNDEuNXEzNyAtNjggOTMgLTExNnExNiAtMTMgMzguNSAtMTF0MzYuNSAxN2wzNSAzNHExNCAxNSAxMi41IDMzLjV0LTE2LjUgMzMuNXEtNDQgNDQgLTg5IDExN3EtMTEgMTggLTI4IDIwdC0zMiAtMTJ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEwNjsiIGQ9Ik01OTIgMGgtMTQ4bDMxIDEyMHEtOTEgMjAgLTE3NS41IDY4LjV0LTE0My41IDEwNi41dC0xMDMuNSAxMTl0LTY2LjUgMTEwdC0yMiA3NnEwIDIxIDE0IDU3LjV0NDIuNSA4Mi41dDY4IDk1dDk0LjUgOTV0MTE2LjUgODIuNXQxNDAgNTl0MTYwLjUgMjIuNXE2MSAwIDEyNiAtMTVsMzIgMTIxaDE0OHpNOTQ0IDc3MGw0NyAxODFxMTA4IC04NSAxNzYuNSAtMTkydDY4LjUgLTE1OXEwIC0yNiAtMTkuNSAtNzF0LTU5LjUgLTEwMnQtOTMgLTExMiB0LTEyOSAtMTA0LjV0LTE1OCAtNzUuNWw0NiAxNzNxNzcgNDkgMTM2IDExN3Q5NyAxMzFxMTEgMTggOSA0Mi41dC0xNCA0MS41cS01NCA3MCAtMTA3IDEzMHpNMzEwIDgyNHEtNzAgLTY5IC0xNjAgLTE4NHEtMTMgLTE2IC0xNSAtNDAuNXQ5IC00Mi41cTE4IC0zMCAzOSAtNjB0NTcgLTcwLjV0NzQgLTczdDkwIC02MXQxMDUgLTQxLjVsNDEgMTU0cS0xMDcgMTggLTE3OC41IDEwMS41dC03MS41IDE5My41cTAgNTkgMjMgMTE0cTggMTkgNC41IDIyIHQtMTcuNSAtMTJ6TTQ0OCA3MjdsLTM1IC0zNnEtMTUgLTE1IC0xOS41IC0zOC41dDQuNSAtNDEuNXEzNyAtNjggOTMgLTExNnExNiAtMTMgMzguNSAtMTF0MzYuNSAxN2wxMiAxMWwyMiA4NmwtMyA0cS00NCA0NCAtODkgMTE3cS0xMSAxOCAtMjggMjB0LTMyIC0xMnoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTE4OyIgZD0iTTIwMCA4MDBsLTIwMCAtNDAwdjYwMGgyMDBxMCA0MSAyOS41IDcwLjV0NzAuNSAyOS41aDMwMHE0MiAwIDcxIC0yOS41dDI5IC03MC41aDUwMHYtMjAwaC0xMDAwek0xNTAwIDcwMGwtMzAwIC03MDBoLTEyMDBsMzAwIDcwMGgxMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMTk7IiBkPSJNNjM1IDExODRsMjMwIC0yNDlxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNWgtMTUwdi02MDFoMTUwcTIxIDAgMjUgLTEwLjV0LTEwIC0yNC41bC0yMzAgLTI0OXEtMTQgLTE1IC0zNSAtMTV0LTM1IDE1bC0yMzAgMjQ5cS0xNCAxNCAtMTAgMjQuNXQyNSAxMC41aDE1MHY2MDFoLTE1MHEtMjEgMCAtMjUgMTAuNXQxMCAyNC41bDIzMCAyNDlxMTQgMTUgMzUgMTV0MzUgLTE1eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxMjA7IiBkPSJNOTM2IDg2NGwyNDkgLTIyOXExNCAtMTUgMTQgLTM1LjV0LTE0IC0zNS41bC0yNDkgLTIyOXEtMTUgLTE1IC0yNS41IC0xMC41dC0xMC41IDI0LjV2MTUxaC02MDB2LTE1MXEwIC0yMCAtMTAuNSAtMjQuNXQtMjUuNSAxMC41bC0yNDkgMjI5cS0xNCAxNSAtMTQgMzUuNXQxNCAzNS41bDI0OSAyMjlxMTUgMTUgMjUuNSAxMC41dDEwLjUgLTI1LjV2LTE0OWg2MDB2MTQ5cTAgMjEgMTAuNSAyNS41dDI1LjUgLTEwLjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEyMTsiIGQ9Ik0xMTY5IDQwMGwtMTcyIDczMnEtNSAyMyAtMjMgNDUuNXQtMzggMjIuNWgtNjcycS0yMCAwIC0zOCAtMjB0LTIzIC00MWwtMTcyIC03MzloMTEzOHpNMTEwMCAzMDBoLTEwMDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTEwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoMTAwMHE0MSAwIDcwLjUgMjkuNXQyOS41IDcwLjV2MTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41ek04MDAgMTAwdjEwMGgxMDB2LTEwMGgtMTAwIHpNMTAwMCAxMDB2MTAwaDEwMHYtMTAwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEyMjsiIGQ9Ik0xMTUwIDExMDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTg1MHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNXQtMzUuNSAxNC41dC0xNC41IDM1LjV2ODUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXpNMTAwMCAyMDBsLTY3NSAyMDBoLTM4bDQ3IC0yNzZxMyAtMTYgLTUuNSAtMjB0LTI5LjUgLTRoLTdoLTg0cS0yMCAwIC0zNC41IDE0dC0xOC41IDM1cS01NSAzMzcgLTU1IDM1MXYyNTB2NnEwIDE2IDEgMjMuNXQ2LjUgMTQgdDE3LjUgNi41aDIwMGw2NzUgMjUwdi04NTB6TTAgNzUwdi0yNTBxLTQgMCAtMTEgMC41dC0yNCA2dC0zMCAxNXQtMjQgMzB0LTExIDQ4LjV2NTBxMCAyNiAxMC41IDQ2dDI1IDMwdDI5IDE2dDI1LjUgN3oiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTM0OyIgZD0iTTYwMCAxMTc4cTExOCAwIDIyNSAtNDUuNXQxODQuNSAtMTIzdDEyMyAtMTg0LjV0NDUuNSAtMjI1dC00NS41IC0yMjV0LTEyMyAtMTg0LjV0LTE4NC41IC0xMjN0LTIyNSAtNDUuNXQtMjI1IDQ1LjV0LTE4NC41IDEyM3QtMTIzIDE4NC41dC00NS41IDIyNXQ0NS41IDIyNXQxMjMgMTg0LjV0MTg0LjUgMTIzdDIyNSA0NS41ek02NzUgOTAwaC0xNTBxLTEwIDAgLTE3LjUgLTcuNXQtNy41IC0xNy41di0yNzVoLTEzN3EtMjEgMCAtMjYgLTExLjUgdDggLTI3LjVsMjIzIC0yNzVxMTMgLTE2IDMyIC0xNnQzMiAxNmwyMjMgMjc1cTEzIDE2IDggMjcuNXQtMjYgMTEuNWgtMTM3djI3NXEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTEzNTsiIGQ9Ik02MDAgMTE3NnExMTYgMCAyMjIuNSAtNDZ0MTg0IC0xMjMuNXQxMjMuNSAtMTg0dDQ2IC0yMjIuNXQtNDYgLTIyMi41dC0xMjMuNSAtMTg0dC0xODQgLTEyMy41dC0yMjIuNSAtNDZ0LTIyMi41IDQ2dC0xODQgMTIzLjV0LTEyMy41IDE4NHQtNDYgMjIyLjV0NDYgMjIyLjV0MTIzLjUgMTg0dDE4NCAxMjMuNXQyMjIuNSA0NnpNNjI3IDExMDFxLTE1IC0xMiAtMzYuNSAtMjAuNXQtMzUuNSAtMTJ0LTQzIC04dC0zOSAtNi41IHEtMTUgLTMgLTQ1LjUgMHQtNDUuNSAtMnEtMjAgLTcgLTUxLjUgLTI2LjV0LTM0LjUgLTM0LjVxLTMgLTExIDYuNSAtMjIuNXQ4LjUgLTE4LjVxLTMgLTM0IC0yNy41IC05MXQtMjkuNSAtNzlxLTkgLTM0IDUgLTkzdDggLTg3cTAgLTkgMTcgLTQ0LjV0MTYgLTU5LjVxMTIgMCAyMyAtNXQyMy41IC0xNXQxOS41IC0xNHExNiAtOCAzMyAtMTV0NDAuNSAtMTV0MzQuNSAtMTJxMjEgLTkgNTIuNSAtMzJ0NjAgLTM4dDU3LjUgLTExIHE3IC0xNSAtMyAtMzR0LTIyLjUgLTQwdC05LjUgLTM4cTEzIC0yMSAyMyAtMzQuNXQyNy41IC0yNy41dDM2LjUgLTE4cTAgLTcgLTMuNSAtMTZ0LTMuNSAtMTR0NSAtMTdxMTA0IC0yIDIyMSAxMTJxMzAgMjkgNDYuNSA0N3QzNC41IDQ5dDIxIDYzcS0xMyA4IC0zNyA4LjV0LTM2IDcuNXEtMTUgNyAtNDkuNSAxNXQtNTEuNSAxOXEtMTggMCAtNDEgLTAuNXQtNDMgLTEuNXQtNDIgLTYuNXQtMzggLTE2LjVxLTUxIC0zNSAtNjYgLTEyIHEtNCAxIC0zLjUgMjUuNXQwLjUgMjUuNXEtNiAxMyAtMjYuNSAxNy41dC0yNC41IDYuNXExIDE1IC0wLjUgMzAuNXQtNyAyOHQtMTguNSAxMS41dC0zMSAtMjFxLTIzIC0yNSAtNDIgNHEtMTkgMjggLTggNThxNiAxNiAyMiAyMnE2IC0xIDI2IC0xLjV0MzMuNSAtNHQxOS41IC0xMy41cTcgLTEyIDE4IC0yNHQyMS41IC0yMC41dDIwIC0xNXQxNS41IC0xMC41bDUgLTNxMiAxMiA3LjUgMzAuNXQ4IDM0LjV0LTAuNSAzMnEtMyAxOCAzLjUgMjkgdDE4IDIyLjV0MTUuNSAyNC41cTYgMTQgMTAuNSAzNXQ4IDMxdDE1LjUgMjIuNXQzNCAyMi41cS02IDE4IDEwIDM2cTggMCAyNCAtMS41dDI0LjUgLTEuNXQyMCA0LjV0MjAuNSAxNS41cS0xMCAyMyAtMzEgNDIuNXQtMzcuNSAyOS41dC00OSAyN3QtNDMuNSAyM3EwIDEgMiA4dDMgMTEuNXQxLjUgMTAuNXQtMSA5LjV0LTQuNSA0LjVxMzEgLTEzIDU4LjUgLTE0LjV0MzguNSAyLjVsMTIgNXE1IDI4IC05LjUgNDZ0LTM2LjUgMjR0LTUwIDE1IHQtNDEgMjBxLTE4IC00IC0zNyAwek02MTMgOTk0cTAgLTE3IDggLTQydDE3IC00NXQ5IC0yM3EtOCAxIC0zOS41IDUuNXQtNTIuNSAxMHQtMzcgMTYuNXEzIDExIDE2IDI5LjV0MTYgMjUuNXExMCAtMTAgMTkgLTEwdDE0IDZ0MTMuNSAxNC41dDE2LjUgMTIuNXoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTc4OyIgZD0iTTkzNSAxMTY1bDI0OCAtMjMwcTE0IC0xNCAxNCAtMzV0LTE0IC0zNWwtMjQ4IC0yMzBxLTE0IC0xNCAtMjQuNSAtMTB0LTEwLjUgMjV2MTUwaC00MDB2MjAwaDQwMHYxNTBxMCAyMSAxMC41IDI1dDI0LjUgLTEwek0yMDAgODAwaC01MHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWg1MHYtMjAwek00MDAgODAwaC0xMDB2MjAwaDEwMHYtMjAwek0xOCA0MzVsMjQ3IDIzMCBxMTQgMTQgMjQuNSAxMHQxMC41IC0yNXYtMTUwaDQwMHYtMjAwaC00MDB2LTE1MHEwIC0yMSAtMTAuNSAtMjV0LTI0LjUgMTBsLTI0NyAyMzBxLTE1IDE0IC0xNSAzNXQxNSAzNXpNOTAwIDMwMGgtMTAwdjIwMGgxMDB2LTIwMHpNMTAwMCA1MDBoNTFxMjAgMCAzNC41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzQuNSAtMTQuNWgtNTF2MjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNzk7IiBkPSJNODYyIDEwNzNsMjc2IDExNnEyNSAxOCA0My41IDh0MTguNSAtNDF2LTExMDZxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2Mzk3cS00IDEgLTExIDV0LTI0IDE3LjV0LTMwIDI5dC0yNCA0MnQtMTEgNTYuNXYzNTlxMCAzMSAxOC41IDY1dDQzLjUgNTJ6TTU1MCAxMjAwcTIyIDAgMzQuNSAtMTIuNXQxNC41IC0yNC41bDEgLTEzdi00NTBxMCAtMjggLTEwLjUgLTU5LjUgdC0yNSAtNTZ0LTI5IC00NXQtMjUuNSAtMzEuNWwtMTAgLTExdi00NDdxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDQ3cS00IDQgLTExIDExLjV0LTI0IDMwLjV0LTMwIDQ2dC0yNCA1NXQtMTEgNjB2NDUwcTAgMiAwLjUgNS41dDQgMTJ0OC41IDE1dDE0LjUgMTJ0MjIuNSA1LjVxMjAgMCAzMi41IC0xMi41dDE0LjUgLTI0LjVsMyAtMTN2LTM1MGgxMDB2MzUwdjUuNXQyLjUgMTIgdDcgMTV0MTUgMTJ0MjUuNSA1LjVxMjMgMCAzNS41IC0xMi41dDEzLjUgLTI0LjVsMSAtMTN2LTM1MGgxMDB2MzUwcTAgMiAwLjUgNS41dDMgMTJ0NyAxNXQxNSAxMnQyNC41IDUuNXoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTk1OyIgZD0iTTYwMCAxMTkxcTEyMCAwIDIyOS41IC00N3QxODguNSAtMTI2dDEyNiAtMTg4LjV0NDcgLTIyOS41dC00NyAtMjI5LjV0LTEyNiAtMTg4LjV0LTE4OC41IC0xMjZ0LTIyOS41IC00N3QtMjI5LjUgNDd0LTE4OC41IDEyNnQtMTI2IDE4OC41dC00NyAyMjkuNXQ0NyAyMjkuNXQxMjYgMTg4LjV0MTg4LjUgMTI2dDIyOS41IDQ3ek02MDAgMTAyMXEtMTE0IDAgLTIxMSAtNTYuNXQtMTUzLjUgLTE1My41dC01Ni41IC0yMTF0NTYuNSAtMjExIHQxNTMuNSAtMTUzLjV0MjExIC01Ni41dDIxMSA1Ni41dDE1My41IDE1My41dDU2LjUgMjExdC01Ni41IDIxMXQtMTUzLjUgMTUzLjV0LTIxMSA1Ni41ek04MDAgNzAwdi0xMDBsLTUwIC01MGwxMDAgLTEwMHYtNTBoLTEwMGwtMTAwIDEwMGgtMTUwdi0xMDBoLTEwMHY0MDBoMzAwek01MDAgNzAwdi0xMDBoMjAwdjEwMGgtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxOTc7IiBkPSJNNTAzIDEwODlxMTEwIDAgMjAwLjUgLTU5LjV0MTM0LjUgLTE1Ni41cTQ0IDE0IDkwIDE0cTEyMCAwIDIwNSAtODYuNXQ4NSAtMjA3dC04NSAtMjA3dC0yMDUgLTg2LjVoLTEyOHYyNTBxMCAyMSAtMTQuNSAzNS41dC0zNS41IDE0LjVoLTMwMHEtMjEgMCAtMzUuNSAtMTQuNXQtMTQuNSAtMzUuNXYtMjUwaC0yMjJxLTgwIDAgLTEzNiA1Ny41dC01NiAxMzYuNXEwIDY5IDQzIDEyMi41dDEwOCA2Ny41cS0yIDE5IC0yIDM3cTAgMTAwIDQ5IDE4NSB0MTM0IDEzNHQxODUgNDl6TTUyNSA1MDBoMTUwcTEwIDAgMTcuNSAtNy41dDcuNSAtMTcuNXYtMjc1aDEzN3EyMSAwIDI2IC0xMS41dC04IC0yNy41bC0yMjMgLTI0NHEtMTMgLTE2IC0zMiAtMTZ0LTMyIDE2bC0yMjMgMjQ0cS0xMyAxNiAtOCAyNy41dDI2IDExLjVoMTM3djI3NXEwIDEwIDcuNSAxNy41dDE3LjUgNy41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxOTg7IiBkPSJNNTAyIDEwODlxMTEwIDAgMjAxIC01OS41dDEzNSAtMTU2LjVxNDMgMTUgODkgMTVxMTIxIDAgMjA2IC04Ni41dDg2IC0yMDYuNXEwIC05OSAtNjAgLTE4MXQtMTUwIC0xMTBsLTM3OCAzNjBxLTEzIDE2IC0zMS41IDE2dC0zMS41IC0xNmwtMzgxIC0zNjVoLTlxLTc5IDAgLTEzNS41IDU3LjV0LTU2LjUgMTM2LjVxMCA2OSA0MyAxMjIuNXQxMDggNjcuNXEtMiAxOSAtMiAzOHEwIDEwMCA0OSAxODQuNXQxMzMuNSAxMzR0MTg0LjUgNDkuNXogTTYzMiA0NjdsMjIzIC0yMjhxMTMgLTE2IDggLTI3LjV0LTI2IC0xMS41aC0xMzd2LTI3NXEwIC0xMCAtNy41IC0xNy41dC0xNy41IC03LjVoLTE1MHEtMTAgMCAtMTcuNSA3LjV0LTcuNSAxNy41djI3NWgtMTM3cS0yMSAwIC0yNiAxMS41dDggMjcuNXExOTkgMjA0IDIyMyAyMjhxMTkgMTkgMzEuNSAxOXQzMi41IC0xOXoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQxOyIgZD0iTTEyMDAgOTAwaC01MHEwIDIxIC00IDM3dC05LjUgMjYuNXQtMTggMTcuNXQtMjIgMTF0LTI4LjUgNS41dC0zMSAydC0zNyAwLjVoLTIwMHYtODUwcTAgLTIyIDI1IC0zNC41dDUwIC0xMy41bDI1IC0ydi0xMDBoLTQwMHYxMDBxNCAwIDExIDAuNXQyNCAzdDMwIDd0MjQgMTV0MTEgMjQuNXY4NTBoLTIwMHEtMjUgMCAtMzcgLTAuNXQtMzEgLTJ0LTI4LjUgLTUuNXQtMjIgLTExdC0xOCAtMTcuNXQtOS41IC0yNi41dC00IC0zN2gtNTB2MzAwIGgxMDAwdi0zMDB6TTUwMCA0NTBoLTI1cTAgMTUgLTQgMjQuNXQtOSAxNC41dC0xNyA3LjV0LTIwIDN0LTI1IDAuNWgtMTAwdi00MjVxMCAtMTEgMTIuNSAtMTcuNXQyNS41IC03LjVoMTJ2LTUwaC0yMDB2NTBxNTAgMCA1MCAyNXY0MjVoLTEwMHEtMTcgMCAtMjUgLTAuNXQtMjAgLTN0LTE3IC03LjV0LTkgLTE0LjV0LTQgLTI0LjVoLTI1djE1MGg1MDB2LTE1MHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjQ2OyIgZD0iTTUwIDEwMDBoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0zNTBoMTAwdjE1MHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjVoNDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xNTBoMTAwdi0xMDBoLTEwMHYtMTUwcTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC00MDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djE1MGgtMTAwdi0zNTBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTQwMCBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djgwMHEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6TTcwMCA3MDB2LTMwMGgzMDB2MzAwaC0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI0NzsiIGQ9Ik0xMDAgMGgtMTAwdjEyMDBoMTAwdi0xMjAwek0yNTAgMTEwMGg0MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtNDAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXY0MDBxMCAyMSAxNC41IDM1LjV0MzUuNSAxNC41ek0zMDAgMTAwMHYtMzAwaDMwMHYzMDBoLTMwMHpNMjUwIDUwMGg5MDBxMjEgMCAzNS41IC0xNC41dDE0LjUgLTM1LjV2LTQwMCBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTkwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yN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<h1 class="title toc-ignore">Replication Output for: Can the Government Deter Discrimination? Evidence from a Randomized Intervention in New York City</h1>
<h4 class="author"><em>Albert H. Fang, Andrew M. Guess, and Macartan Humphreys</em></h4>
<h4 class="date"><em>December 2, 2017</em></h4>

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<div id="TOC">
<ul>
<li><a href="#tables-and-figures-in-main-text"><span class="toc-section-number">1</span> Tables and Figures in Main Text</a><ul>
<li><a href="#figure-1-main-results-on-discrimination-levels-and-treatment-effects"><span class="toc-section-number">1.1</span> Figure 1: Main Results on Discrimination Levels and Treatment Effects</a><ul>
<li><a href="#code-for-analyses"><span class="toc-section-number">1.1.1</span> Code for Analyses</a></li>
<li><a href="#code-for-figure-construction"><span class="toc-section-number">1.1.2</span> Code for Figure Construction</a></li>
</ul></li>
<li><a href="#figure-2-posterior-densities-of-treatment-effects-on-discrimination"><span class="toc-section-number">1.2</span> Figure 2: Posterior Densities of Treatment Effects on Discrimination</a></li>
<li><a href="#randomization-check-reported-in-main-text-footnote-17"><span class="toc-section-number">1.3</span> Randomization Check (reported in main text, footnote 17)</a></li>
</ul></li>
<li><a href="#appendix-tables-and-figures"><span class="toc-section-number">2</span> Appendix Tables and Figures</a><ul>
<li><a href="#table-a1-p.a-2-distribution-of-experimental-subjects-by-randomization-block"><span class="toc-section-number">2.1</span> Table A1 (p. A-2): Distribution of Experimental Subjects by Randomization Block</a></li>
<li><a href="#table-a2-p.a-3-incidence-of-early-stage-discrimination"><span class="toc-section-number">2.2</span> Table A2 (p. A-3): Incidence of Early Stage Discrimination</a></li>
<li><a href="#figure-a2-p.a-5-cumulative-number-of-cases-over-implementation-period"><span class="toc-section-number">2.3</span> Figure A2 (p. A-5): Cumulative Number of Cases Over Implementation Period</a></li>
<li><a href="#figure-a3-p.a-6-discrimination-levels-and-treatment-effects-using-the-subjective-discrimination-index"><span class="toc-section-number">2.4</span> Figure A3 (p. A-6): Discrimination Levels and Treatment Effects using the Subjective Discrimination Index</a></li>
<li><a href="#figure-a4-p.a-17-estimates-of-tester-random-effects-on-outcome-measures"><span class="toc-section-number">2.5</span> Figure A4 (p. A-17): Estimates of Tester Random Effects on Outcome Measures</a></li>
<li><a href="#table-a4-p.a-20-selected-characteristics-of-housing-units-in-the-audit-and-experimental-samples"><span class="toc-section-number">2.6</span> Table A4 (p. A-20): Selected Characteristics of Housing Units in the Audit and Experimental Samples</a></li>
<li><a href="#table-a5-p.a-21-distribution-of-rental-units-across-boroughs-by-sample"><span class="toc-section-number">2.7</span> Table A5 (p. A-21): Distribution of Rental Units Across Boroughs, by Sample</a></li>
<li><a href="#figure-a5-p.a-22-map-of-the-geographic-distribution-of-housing-units-corresponding-to-advertised-listings"><span class="toc-section-number">2.8</span> Figure A5 (p. A-22): Map of the Geographic Distribution of Housing Units Corresponding to Advertised Listings</a></li>
<li><a href="#table-a6-p.a-24-baseline-incidence-of-discrimination-in-person-and-post-visit"><span class="toc-section-number">2.9</span> Table A6 (p. A-24): Baseline Incidence of Discrimination: In-Person and Post-Visit</a></li>
<li><a href="#table-a7-p.a-25-estimated-effects-of-messaging-on-net-discrimination-levels"><span class="toc-section-number">2.10</span> Table A7 (p. A-25): Estimated Effects of Messaging on Net Discrimination Levels</a></li>
<li><a href="#table-a8-p.a-26-unweighted-itt-estimates-of-messaging-on-net-discrimination-in-receiving-a-post-visit-callback"><span class="toc-section-number">2.11</span> Table A8 (p. A-26): Unweighted ITT Estimates of Messaging on Net Discrimination in Receiving a Post-Visit Callback</a></li>
<li><a href="#table-a9-p.a-27-unweighted-itt-estimates-of-messaging-on-net-discrimination-in-receiving-a-post-visit-offer-for-the-unit"><span class="toc-section-number">2.12</span> Table A9 (p. A-27): Unweighted ITT Estimates of Messaging on Net Discrimination in Receiving a Post-Visit Offer for the Unit</a></li>
<li><a href="#table-a10-p.a-28-sensitivity-analysis-estimated-effects-of-messaging-on-net-discrimination-levels-three-group-parametric-estimator"><span class="toc-section-number">2.13</span> Table A10 (p. A-28): Sensitivity Analysis: Estimated Effects of Messaging on Net Discrimination Levels (Three-Group Parametric Estimator)</a></li>
<li><a href="#table-a11-p.a-29-predicted-means-differences-and-percent-differences-from-two-group-parametric-estimators"><span class="toc-section-number">2.14</span> Table A11 (p. A-29): Predicted Means, Differences, and Percent Differences from Two-Group Parametric Estimators</a></li>
<li><a href="#table-a12-p.a-31-incidence-of-early-stage-discrimination-subjective-measures"><span class="toc-section-number">2.15</span> Table A12 (p. A-31): Incidence of Early Stage Discrimination: Subjective Measures</a></li>
<li><a href="#table-a13-p.a-32-treatment-noncompliance-incidence"><span class="toc-section-number">2.16</span> Table A13 (p. A-32): Treatment Noncompliance Incidence</a></li>
<li><a href="#table-a14-p.a-34-estimated-complier-average-causal-effects-of-messages-on-net-discrimination-levels"><span class="toc-section-number">2.17</span> Table A14 (p. A-34): Estimated Complier Average Causal Effects of Messages on Net Discrimination Levels</a></li>
<li><a href="#table-a15-p.a-36-covariate-adjusted-itt-estimates"><span class="toc-section-number">2.18</span> Table A15 (p. A-36): Covariate Adjusted ITT Estimates</a><ul>
<li><a href="#lasso-regression-for-covariate-selection"><span class="toc-section-number">2.18.1</span> Lasso Regression for Covariate Selection</a></li>
<li><a href="#covariate-adjusted-itt-estimation"><span class="toc-section-number">2.18.2</span> Covariate Adjusted ITT Estimation</a></li>
</ul></li>
<li><a href="#table-a16-p.a-37-missingness-analysis-estimated-correlation-between-treatment-assignment-and-missingness-on-subject-index-measure"><span class="toc-section-number">2.19</span> Table A16 (p. A-37): Missingness Analysis: Estimated Correlation between Treatment Assignment and Missingness on Subject Index Measure</a></li>
<li><a href="#table-a17-p.a-37-missingness-analysis-pairwise-correlations-between-missing-subjective-index-measures-and-objective-net-discrimination-measures"><span class="toc-section-number">2.20</span> Table A17 (p. A-37): Missingness Analysis: Pairwise Correlations between Missing Subjective Index Measures and Objective Net Discrimination Measures</a></li>
<li><a href="#table-a18-p.a-37-missingness-analysis-predicting-missingness-on-subjective-indicators-as-a-function-of-tester-race"><span class="toc-section-number">2.21</span> Table A18 (p. A-37): Missingness Analysis: Predicting Missingness on Subjective Indicators As a Function of Tester Race</a></li>
<li><a href="#table-a19-p.a-38-balance-table-for-categorical-covariates"><span class="toc-section-number">2.22</span> Table A19 (p. A-38): Balance Table for Categorical Covariates</a></li>
<li><a href="#table-a20-p.a-38-to-a-44-balance-table-for-continuous-covariates"><span class="toc-section-number">2.23</span> Table A20 (p. A-38 to A-44): Balance Table for Continuous Covariates</a></li>
<li><a href="#table-a21-p.a-46-sensitivity-analysis-estimated-effects-of-messaging-on-net-discrimination-levels-among-subsample-excluding-likely-discrimination-cases"><span class="toc-section-number">2.24</span> Table A21 (p. A-46): Sensitivity Analysis: Estimated Effects of Messaging on Net Discrimination Levels Among Subsample Excluding Likely Discrimination Cases</a></li>
<li><a href="#table-a22-p.a-47-distribution-of-subjects-by-their-perceived-race"><span class="toc-section-number">2.25</span> Table A22 (p. A-47): Distribution of Subjects by their Perceived Race</a></li>
<li><a href="#figure-a6-p.a-48-estimated-effects-of-monitoring-messaging-on-net-discrimination-levels-relative-to-control-by-the-perceived-race-of-the-landlordbroker"><span class="toc-section-number">2.26</span> Figure A6 (p. A-48): Estimated Effects of Monitoring Messaging on Net Discrimination Levels Relative to Control, by the Perceived Race of the Landlord/Broker</a></li>
<li><a href="#figure-a7-p.a-49-estimated-effects-of-punitive-messaging-on-net-discrimination-levels-relative-to-control-by-the-perceived-race-of-the-landlordbroker"><span class="toc-section-number">2.27</span> Figure A7 (p. A-49): Estimated Effects of Punitive Messaging on Net Discrimination Levels Relative to Control, by the Perceived Race of the Landlord/Broker</a></li>
<li><a href="#appendix-e.15-p.a-50-analyses-addressing-spillover-concerns"><span class="toc-section-number">2.28</span> Appendix E.15 (p. A-50): Analyses Addressing Spillover Concerns</a></li>
<li><a href="#appendix-e.16-p.a-51-joint-distribution-of-the-number-of-testers-in-matched-trios-who-receive-a-callback-and-an-offer"><span class="toc-section-number">2.29</span> Appendix E.16 (p. A-51): Joint Distribution of the Number of Testers in Matched Trios Who Receive a Callback and an Offer</a></li>
</ul></li>
</ul>
</div>

<p><strong>IMPORTANT NOTES (PLEASE READ FIRST):</strong></p>
<ul>
<li>This Rmd file produces the tables, figures, and analyses reported in the manuscript and in the online appendix. This output file shows the core code and analyses used, as well as the main output from these analyses.</li>
<li>At various points in this Rmd file, we have set <code>echo=F</code> and <code>eval=F</code> in selected R code chunks to suppress displaying tex output in the Rmd output. To have these code chunks produce .tex table output files, set <code>eval=T</code>.</li>
<li>You must specify your local root directory (in the first code chunk).</li>
<li>The code in <code>code_0_read_data.R</code> currently loads data from the local directory. If you want to load data from Dataverse, in the second code chunk you must (1) set <code>local_data=FALSE</code> and (2) set your Dataverse API token in the line <code>Sys.setenv(&quot;DATAVERSE_KEY&quot; = &quot;ENTER-YOUR-API-TOKEN-HERE&quot;)</code>.</li>
</ul>
<div id="tables-and-figures-in-main-text" class="section level1">
<h1><span class="header-section-number">1</span> Tables and Figures in Main Text</h1>
<div id="figure-1-main-results-on-discrimination-levels-and-treatment-effects" class="section level2">
<h2><span class="header-section-number">1.1</span> Figure 1: Main Results on Discrimination Levels and Treatment Effects</h2>
<div id="code-for-analyses" class="section level3">
<h3><span class="header-section-number">1.1.1</span> Code for Analyses</h3>
<pre class="r"><code># ---------------------------------------------------------------------- #
# DEFINE COLNAMES FOR OUTPUT TABLES

col.labels &lt;- c(&quot;Outcome&quot;,c(&quot;Estimate&quot;,&quot;SE&quot;,&quot;t&quot;,&quot;P&quot;))

# ---------------------------------------------------------------------- #
# MODELS WITH ONLY BLOCK FIXED EFFECTS

itt.wb.mc &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)
itt.wb.pc &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)
itt.wb.pm &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)

itt.wh.mc &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)
itt.wh.pc &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)
itt.wh.pm &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)

itt.bh.mc &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)
itt.bh.pc &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)
itt.bh.pm &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)

fit.wb.mc &lt;- fit.wb.pc &lt;- fit.wb.pm &lt;- fit.wh.mc &lt;- fit.wh.pc &lt;- fit.wh.pm &lt;- fit.bh.mc &lt;- fit.bh.pc &lt;- fit.bh.pm &lt;- list() # store results from ITT est w/ block FE (no other covs)

for(i in 1:length(outvars.wb)){
  model.mc &lt;- paste(outvars.wb[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)

  fit.wb.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.wb.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.wb.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)

  itt.wb.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.wb.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.wb.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]

  itt.wb.mc[i,5] &lt;- pt(coef(summary(fit.mc))[,3], summary(fit.mc)$df[2], lower=TRUE)[2] #one sided p
  itt.wb.pc[i,5] &lt;- pt(coef(summary(fit.pc))[,3], summary(fit.pc)$df[2], lower=TRUE)[2] #one sided p
}

for(i in 1:length(outvars.wh)){
  model.mc &lt;- paste(outvars.wh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)

  fit.wh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.wh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.wh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)

  itt.wh.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.wh.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.wh.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]

  itt.wh.mc[i,5] &lt;- pt(coef(summary(fit.mc))[,3], summary(fit.mc)$df[2], lower=TRUE)[2] #one sided p
  itt.wh.pc[i,5] &lt;- pt(coef(summary(fit.pc))[,3], summary(fit.pc)$df[2], lower=TRUE)[2] #one sided p
}

for(i in 1:length(outvars.bh)){
  model.mc &lt;- paste(outvars.bh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)

  fit.bh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.bh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.bh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)

  itt.bh.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.bh.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.bh.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
}

# Add outcome measure labels
itt.wb.mc[,1] &lt;- outvars.wb.labs
itt.wb.pc[,1] &lt;- outvars.wb.labs
itt.wb.pm[,1] &lt;- outvars.wb.labs

itt.wh.mc[,1] &lt;- outvars.wh.labs
itt.wh.pc[,1] &lt;- outvars.wh.labs
itt.wh.pm[,1] &lt;- outvars.wh.labs

itt.bh.mc[,1] &lt;- outvars.bh.labs
itt.bh.pc[,1] &lt;- outvars.bh.labs
itt.bh.pm[,1] &lt;- outvars.bh.labs

# Add column names
colnames(itt.wb.mc) &lt;- colnames(itt.wb.pc) &lt;- colnames(itt.wb.pm) &lt;- col.labels
colnames(itt.wh.mc) &lt;- colnames(itt.wh.pc) &lt;- colnames(itt.wh.pm) &lt;- col.labels
colnames(itt.bh.mc) &lt;- colnames(itt.bh.pc) &lt;- colnames(itt.bh.pm) &lt;- col.labels

# Stack, create output table
out2 &lt;- rbind(itt.wb.mc, itt.wh.mc, itt.bh.mc,
              itt.wb.pc, itt.wh.pc, itt.bh.pc,
              itt.wb.pm, itt.wh.pm, itt.bh.pm)

out2 &lt;- out2[-seq(1,33,4),]

#----- CIs -----#

df.wb.mc &lt;- unlist(lapply(fit.wb.mc, function(x) summary(x)$df[2]))[2:4]
df.wb.pc &lt;- unlist(lapply(fit.wb.pc, function(x) summary(x)$df[2]))[2:4]
df.wb.pm &lt;- unlist(lapply(fit.wb.pm, function(x) summary(x)$df[2]))[2:4]

df.wh.mc &lt;- unlist(lapply(fit.wh.mc, function(x) summary(x)$df[2]))[2:4]
df.wh.pc &lt;- unlist(lapply(fit.wh.pc, function(x) summary(x)$df[2]))[2:4]
df.wh.pm &lt;- unlist(lapply(fit.wh.pm, function(x) summary(x)$df[2]))[2:4]

df.bh.mc &lt;- unlist(lapply(fit.bh.mc, function(x) summary(x)$df[2]))[2:4]
df.bh.pc &lt;- unlist(lapply(fit.bh.pc, function(x) summary(x)$df[2]))[2:4]
df.bh.pm &lt;- unlist(lapply(fit.bh.pm, function(x) summary(x)$df[2]))[2:4]

df.2g.10 &lt;- c(df.wb.mc, df.wh.mc, df.bh.mc)
df.2g.20 &lt;- c(df.wb.pc, df.wh.pc, df.bh.pc)
df.2g.21 &lt;- c(df.wb.pm, df.wh.pm, df.bh.pm)

cv.2g.10 &lt;- qt(p=0.025, df=df.2g.10, lower.tail=TRUE)
cv.2g.20 &lt;- qt(p=0.025, df=df.2g.20, lower.tail=TRUE)
cv.2g.21 &lt;- qt(p=0.025, df=df.2g.21, lower.tail=TRUE)

cv.2g &lt;- c(cv.2g.10, cv.2g.20, cv.2g.21)
itt.2g.ci &lt;- as.numeric(out2[,2]) + abs(cv.2g)*cbind(-as.numeric(out2[,3]), as.numeric(out2[,3]))

# without rounding
#cbind(apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, x[1] ,&quot;, &quot;, x[2], &quot;]&quot;, sep=&quot;&quot;)))

# with rounding
#cbind(apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, round(x[1],3) ,&quot;, &quot;, round(x[2],3), &quot;]&quot;, sep=&quot;&quot;)))

# Round and format results
for(i in 2:ncol(out2)) out2[,i] &lt;- round(as.numeric(out2[,i]),3)
for(i in 5) out2[,i] &lt;- paste(&quot;(&quot;,out2[,i],&quot;)&quot;,sep=&quot;&quot;)

# Assemble results
itt.2g.tab &lt;- cbind(out2, cbind(apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, round(x[1],3) ,&quot;, &quot;, round(x[2],3), &quot;]&quot;, sep=&quot;&quot;))))
colnames(itt.2g.tab) &lt;- c(&quot;Outcome&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;t&quot;, &quot;p-value&quot;, &quot;95% CI&quot;)
# Save results
write.csv(itt.2g.tab, &quot;out_tablea7_itt_2g_blockfe_nocovs_UNFORMATTED.csv&quot;, row.names=FALSE)

# Prep results for plotting
itt.est &lt;- read.csv(&quot;out_tablea7_itt_2g_blockfe_nocovs_UNFORMATTED.csv&quot;, header=TRUE, stringsAsFactors=FALSE)

itt.est$lower.ci &lt;- as.numeric(sapply(strsplit(itt.est[,6], &quot;, &quot;), function(x) gsub(&quot;[&quot;, &quot;&quot;, x[1], fixed=TRUE)))
itt.est$upper.ci &lt;- as.numeric(sapply(strsplit(itt.est[,6], &quot;, &quot;), function(x) gsub(&quot;]&quot;, &quot;&quot;, x[2], fixed=TRUE)))

row_labels &lt;- c(&quot;W-B&quot;, &quot;W-H&quot;, &quot;B-H&quot;)
col_labels &lt;- c(&quot;M-C&quot;, &quot;P-C&quot;, &quot;P-M&quot;)

cb.itt &lt;- itt.est[grepl(&quot;callback&quot;, itt.est[,1], fixed=TRUE),]
off.itt &lt;- itt.est[grepl(&quot;offer&quot;, itt.est[,1], fixed=TRUE),]
meindex.itt &lt;- itt.est[grepl(&quot;Index&quot;, itt.est[,1], fixed=TRUE),]

cb.reg &lt;- list(coef=matrix(cb.itt$Estimate, nrow=3, ncol=3, byrow=FALSE),
               se=matrix(cb.itt$SE, nrow=3, ncol=3, byrow=FALSE),
               lower=matrix(cb.itt$lower.ci, nrow=3, ncol=3, byrow=FALSE),
               upper=matrix(cb.itt$upper.ci, nrow=3, ncol=3, byrow=FALSE))

off.reg &lt;- list(coef=matrix(off.itt$Estimate, nrow=3, ncol=3, byrow=FALSE),
               se=matrix(off.itt$SE, nrow=3, ncol=3, byrow=FALSE),
               lower=matrix(off.itt$lower.ci, nrow=3, ncol=3, byrow=FALSE),
               upper=matrix(off.itt$upper.ci, nrow=3, ncol=3, byrow=FALSE))

meindex.reg &lt;- list(coef=matrix(meindex.itt$Estimate, nrow=3, ncol=3, byrow=FALSE),
               se=matrix(meindex.itt$SE, nrow=3, ncol=3, byrow=FALSE),
               lower=matrix(meindex.itt$lower.ci, nrow=3, ncol=3, byrow=FALSE),
               upper=matrix(meindex.itt$upper.ci, nrow=3, ncol=3, byrow=FALSE))

#===============#
# Create basic.comparisons.table function used to generate Figure 1
#===============#

# Reports all differences and ses and ps for non parametric comparisons
# All based on t-tests

basic.comparisons.table = function(
  yroot     = &quot;cb&quot; ,    # Indicate root of y variable name
  row_reorder = 1:6
){
  Y.B &lt;- dat[paste(yroot, &quot;_A&quot;, sep=&quot;&quot;)][,1]  # outcome for blacks
  Y.H &lt;- dat[paste(yroot, &quot;_B&quot;, sep=&quot;&quot;)][,1]  # outcome for hispanics
  Y.W &lt;- dat[paste(yroot, &quot;_C&quot;, sep=&quot;&quot;)][,1]  # outcome for whites   
  W   &lt;- dat$ipw  # Weight 
  T   &lt;- dat$TA   # Treatment
  rnames &lt;- c(&quot;B&quot;,&quot;H&quot;,&quot;W&quot;,&quot;B-H&quot;,&quot;W-B&quot;,&quot;W-H&quot;)  
  cnames &lt;-   c(&quot;Control&quot;, &quot;Monitoring&quot;, &quot;Punitive&quot;, &quot;M-C&quot;, &quot;P-C&quot;, &quot;P-M&quot;)
  
  O &lt;- rbind(
    sapply(0:2, function(c) weighted.mean(Y.B[T==c], W[T==c])),
    sapply(0:2, function(c) weighted.mean(Y.H[T==c], W[T==c])),
    sapply(0:2, function(c) weighted.mean(Y.W[T==c], W[T==c])))
  
  O &lt;- cbind(O, O[,2] -O[,1],  O[,3] -O[,1],  O[,3] -O[,2])
  O &lt;- rbind(O, O[1,] -O[2,],  O[3,] -O[1,],  O[3,] -O[2,])
  colnames(O) &lt;- cnames
  rownames(O) &lt;- rnames
  
  
  # Standard Errors and ps and dfs and CIs: Theses are worked out in four blocks
  # Block [1,1]
  main.se &lt;- rbind(
    sapply(0:2, function(c) wtd.var(Y.B[T==c], W[T==c])/((sum(T==c)^.5))),
    sapply(0:2, function(c) wtd.var(Y.H[T==c], W[T==c])/((sum(T==c)^.5))),
    sapply(0:2, function(c) wtd.var(Y.W[T==c], W[T==c])/((sum(T==c)^.5)))
  )
  
  main.df &lt;- rbind(
    sapply(0:2, function(c) length(Y.B[T==c])-1),
    sapply(0:2, function(c) length(Y.H[T==c])-1),
    sapply(0:2, function(c) length(Y.W[T==c])-1))
  
  main.cv &lt;- apply(main.df, 1, function(j) abs(qt(p=.025, df=j, lower.tail=TRUE)))
  
  main.levels &lt;- O[1:3,1:3]
  main.lower &lt;- main.upper &lt;- matrix(NA, nrow=nrow(main.se), ncol=ncol(main.se))
  for(u in 1:ncol(main.se)){
    for(v in 1:nrow(main.se)){
      main.lower[v,u] &lt;- main.levels[v,u] - main.cv[v,u]*main.se[v,u]
      main.upper[v,u] &lt;- main.levels[v,u] + main.cv[v,u]*main.se[v,u]
    }
  }
  
  # Block [2,1] Disrimination
  discrimination.p &lt;- rbind(
    sapply(0:2, function(c) wtd.t.test((Y.B-Y.H)[T==c], weight=W[T==c])$coefficient[3]),
    sapply(0:2, function(c) wtd.t.test((Y.W-Y.B)[T==c], weight=W[T==c])$coefficient[3]),
    sapply(0:2, function(c) wtd.t.test((Y.W-Y.H)[T==c], weight=W[T==c])$coefficient[3]))
  
  discrimination.se &lt;- rbind(
    sapply(0:2, function(c) wtd.t.test((Y.B-Y.H)[T==c], weight=W[T==c])$additional[4]),
    sapply(0:2, function(c) wtd.t.test((Y.W-Y.B)[T==c], weight=W[T==c])$additional[4]),
    sapply(0:2, function(c) wtd.t.test((Y.W-Y.H)[T==c], weight=W[T==c])$additional[4]))
  
  discrimination.df &lt;- rbind(
    sapply(0:2, function(c) wtd.t.test((Y.B-Y.H)[T==c], weight=W[T==c])$coefficient[2]),
    sapply(0:2, function(c) wtd.t.test((Y.W-Y.B)[T==c], weight=W[T==c])$coefficient[2]),
    sapply(0:2, function(c) wtd.t.test((Y.W-Y.H)[T==c], weight=W[T==c])$coefficient[2]))
  
  discrimination.cv &lt;- apply(discrimination.df, 1, function(j) abs(qt(p=.025, df=j, lower.tail=TRUE)))
  
  discrimination.levels &lt;- O[4:6,1:3]
  discrimination.lower &lt;- discrimination.upper &lt;- matrix(NA, nrow=nrow(discrimination.se), ncol=ncol(discrimination.se))
  for(u in 1:ncol(discrimination.se)){
    for(v in 1:nrow(discrimination.se)){
      discrimination.lower[v,u] &lt;- discrimination.levels[v,u] - discrimination.cv[v,u]*discrimination.se[v,u]
      discrimination.upper[v,u] &lt;- discrimination.levels[v,u] + discrimination.cv[v,u]*discrimination.se[v,u]
    }
  }
  
  # Block [1,2] Main Treatment Effects Discrimination
  get.p.se = function(Y, a,b, gets = 1) {
    # set gets = 1 for coefficient, 2 for se, 3 for t and 4 for p
    MM &lt;- lm(Y[T==a | T==b]  ~ (T==a)[T==a | T==b], weight = W[T==a | T==b])
    coef(summary(MM))[2,gets] 
  }
  
  matrix.e &lt;- function(gets) {rbind(
    c( get.p.se(Y.B, 0,1, gets=gets), get.p.se(Y.B, 0,2, gets=gets), get.p.se(Y.B, 1,2, gets=gets)),
    c( get.p.se(Y.H, 0,1, gets=gets), get.p.se(Y.H, 0,2, gets=gets), get.p.se(Y.H, 1,2, gets=gets)),
    c( get.p.se(Y.W, 0,1, gets=gets), get.p.se(Y.W, 0,2, gets=gets), get.p.se(Y.W, 1,2, gets=gets))
  )}
  
  effects.p &lt;- matrix.e(4)
  effects.se &lt;- matrix.e(2)
  
  get.df = function(Y, a, b) {
    MM &lt;- lm(Y[T==a | T==b]  ~ (T==a)[T==a | T==b], weight = W[T==a | T==b])
    summary(MM)$df[2]
  }
  
  matrix.df &lt;- function(x) { rbind(
    c( get.df(Y.B, 0,1), get.df(Y.B, 0,2), get.df(Y.B, 1,2)),
    c( get.df(Y.H, 0,1), get.df(Y.H, 0,2), get.df(Y.H, 1,2)),
    c( get.df(Y.W, 0,1), get.df(Y.W, 0,2), get.df(Y.W, 1,2))
  )}
  
  
  diff.df &lt;- matrix.df()
  diff.cv &lt;- apply(diff.df, 1, function(j) abs(qt(p=.025, df=j, lower.tail=TRUE)))
  
  diff.levels &lt;- O[1:3,4:6]
  diff.lower &lt;- diff.upper &lt;- matrix(NA, nrow=nrow(effects.se), ncol=ncol(effects.se))
  for(u in 1:ncol(effects.se)){
    for(v in 1:nrow(effects.se)){
      diff.lower[v,u] &lt;- diff.levels[v,u] - diff.cv[v,u]*effects.se[v,u]
      diff.upper[v,u] &lt;- diff.levels[v,u] + diff.cv[v,u]*effects.se[v,u]
    }
  }
  
  
  # Block [2,2]: Discrimination Effects
  d.get.p.se = function(Yi, Yj, a,b, gets = 1) {
    # set gets = 1 for coefficient, 2 for se, 3 for t and 4 for p
    MM &lt;- lm(Yj[T==a | T==b] - Yi[T==a | T==b]  ~ (T==a)[T==a | T==b], weight = W[T==a | T==b])
    coef(summary(MM))[2,gets] 
  }
  
  d.matrix.e &lt;- function(gets) {rbind(
    c( d.get.p.se(Y.H, Y.B, 0,1, gets=gets), d.get.p.se(Y.H, Y.B, 0,2, gets=gets), d.get.p.se(Y.H, Y.B, 1,2, gets=gets)),
    c( d.get.p.se(Y.B, Y.W, 0,1, gets=gets), d.get.p.se(Y.B, Y.W, 0,2, gets=gets), d.get.p.se(Y.B, Y.W, 1,2, gets=gets)),
    c( d.get.p.se(Y.H, Y.W, 0,1, gets=gets), d.get.p.se(Y.H, Y.W, 0,2, gets=gets), d.get.p.se(Y.H, Y.W, 1,2, gets=gets))
  )}
  
  d.effects.p &lt;- d.matrix.e(4)
  d.effects.se &lt;- d.matrix.e(2)
  
  get.itt.df = function(Yi, Yj, a, b) {
    MM &lt;- lm(Yj[T==a | T==b] - Yi[T==a | T==b]  ~ (T==a)[T==a | T==b], weight = W[T==a | T==b])
    summary(MM)$df[2]
  }
  
  matrix.itt.df &lt;- function(x) { rbind(
    c( d.get.p.se(Y.H, Y.B, 0,1), d.get.p.se(Y.H, Y.B, 0,2), d.get.p.se(Y.H, Y.B, 1,2)),
    c( d.get.p.se(Y.B, Y.W, 0,1), d.get.p.se(Y.B, Y.W, 0,2), d.get.p.se(Y.B, Y.W, 1,2)),
    c( d.get.p.se(Y.H, Y.W, 0,1), d.get.p.se(Y.H, Y.W, 0,2), d.get.p.se(Y.H, Y.W, 1,2))
  )}
  
  
  itt.df &lt;- matrix.df()
  itt.cv &lt;- apply(itt.df, 1, function(j) abs(qt(p=.025, df=j, lower.tail=TRUE)))
  
  itt.levels &lt;- O[4:6,4:6]
  itt.lower &lt;- itt.upper &lt;- matrix(NA, nrow=nrow(d.effects.se), ncol=ncol(d.effects.se))
  for(u in 1:ncol(d.effects.se)){
    for(v in 1:nrow(d.effects.se)){
      itt.lower[v,u] &lt;- itt.levels[v,u] - itt.cv[v,u]*d.effects.se[v,u]
      itt.upper[v,u] &lt;- itt.levels[v,u] + itt.cv[v,u]*d.effects.se[v,u]
    }
  }
  
  # Stitch together SE and p
  SE &lt;- rbind(cbind(main.se, effects.se),
              cbind(discrimination.se, d.effects.se))
  
  P &lt;- rbind(cbind(matrix(NA, 3,3), effects.p),
             cbind(discrimination.p, d.effects.p))
  colnames(SE) &lt;- cnames; rownames(SE) &lt;- rnames
  colnames(P) &lt;- cnames;  rownames(P) &lt;- rnames
  
  
  # Stitch together CIs
  
  LCI &lt;- rbind(cbind(main.lower,diff.lower),
               cbind(discrimination.lower,itt.lower))
  UCI &lt;- rbind(cbind(main.upper,diff.upper),
               cbind(discrimination.upper,itt.upper))
  colnames(LCI) &lt;- colnames(UCI) &lt;- cnames
  rownames(LCI) &lt;- rownames(UCI) &lt;- rnames
  
  # Reorder rows as specified
  
  O &lt;- O[row_reorder,]
  SE &lt;- SE[row_reorder,]
  P &lt;- P[row_reorder,]
  LCI &lt;- LCI[row_reorder,]
  UCI &lt;- UCI[row_reorder,]
  
  # Return list object
  
  list(coef=O, se = SE, p =P, lower=LCI, upper=UCI)
}


# Run basic.comparisons.table to generate data to populate graphs

cb &lt;- basic.comparisons.table(yroot=&quot;cb&quot;, row_reorder=c(3,1,2,5,6,4))
off &lt;- basic.comparisons.table(yroot=&quot;off&quot;, row_reorder=c(3,1,2,5,6,4))
meindex &lt;- basic.comparisons.table(yroot=&quot;meindex.impute&quot;, row_reorder=c(3,1,2,5,6,4))</code></pre>
</div>
<div id="code-for-figure-construction" class="section level3">
<h3><span class="header-section-number">1.1.2</span> Code for Figure Construction</h3>
<pre class="r"><code># HELPER FUNCTIONS

#================#
# Plot function used in Figure 1
#================#

# Take matrices CB, OFF, MEINDEX
# Take matrices CB.REG, OFF.REG, MEINDEX.REG

# plot with a single set of three or two threes
plot.helper &lt;- function(coefs1=1:3, sds1=3:5, lower1, upper1, coefs2=NA, sds2=NA, lower2, upper2, double=FALSE, xlim=NULL ){
  coefplot(coefs1, sds1, CI=2, lower.conf.bounds=lower1, upper.conf.bounds=upper1, pch.pts=21, varnames=&quot;&quot;, main = &quot;&quot;, h.axis = FALSE, xlim = xlim, cex.pts=3, lwd=3)
  if(double) coefplot(coefs2, sds2, CI=2, lower.conf.bounds=lower2, upper.conf.bounds=upper2, add=TRUE, pch.pts=19, cex.pts=3, lwd=3)
  axis(side=1, line=2, cex.axis=1.5, padj=1)
}


itt.plot &lt;- function(y,y2,xlim){
  for(i in 1:3) plot.helper(coefs1=rev(y$coef[1:3,i]), sds1=rev(y$se[1:3,i]), xlim=xlim[[1]], lower1=rev(y$lower[1:3,i]), upper1=rev(y$upper[1:3,i]))
  for(i in 4:6) plot.helper(coefs1=rev(y$coef[1:3,i]), sds1=rev(y$se[1:3,i]), xlim=xlim[[2]], lower1=rev(y$lower[1:3,i]), upper1=rev(y$upper[1:3,i]))
  for(i in 1:3) {
    plot.helper(coefs1=rev(y$coef[4:6,i]), sds1=rev(y$se[4:6,i]), xlim=xlim[[3]], lower1=rev(y$lower[4:6,i]), upper1=rev(y$upper[4:6,i]))
    if ( i == 1 ) {  # highlight control group
      abline(h=3, col=rgb(0,0,0,.1), lwd=40)
      abline(h=2, col=rgb(0,0,0,.1), lwd=40)
    }
  }
  for(i in 4:6) {
    plot.helper(coefs1=rev(y$coef[4:6,i]), sds1=rev(y$se[4:6,i]), 
                lower1=rev(y$lower[4:6,i]), upper1=rev(y$upper[4:6,i]),
                coefs2=rev(y2$coef[1:3,(i-3)]), sds2=rev(y2$se[1:3,(i-3)]), 
                lower2=rev(y2$lower[1:3,(i-3)]), upper2=rev(y2$upper[1:3,(i-3)]),
                double=TRUE, xlim=xlim[[4]])
    if ( i == 5 ){  # highlight P-C
      abline(h=3, col=rgb(0,0,0,.1), lwd=40)
      abline(h=2, col=rgb(0,0,0,.1), lwd=40)
    }
  }
}


# FIGURE SPECS
col_width &lt;- 7
row_height &lt;- 4
width_spec &lt;- c(4, 4, rep(col_width, 3), 2, rep(col_width, 3))
height_spec &lt;- c(2, 2, row_height, 2, row_height)
colhead.cex &lt;- 2.7 # font size for column headings
colhead2.cex &lt;- 2.7 # font size for treatment headings
rowsub.cex &lt;- 3  # font size for variable labels
col_headings &lt;- c(&quot;Control&quot;, &quot;Monitoring&quot;, &quot;Punitive&quot;, &quot;Monitoring\nvs. Control&quot;, &quot;Punitive\nvs. Control&quot;, &quot;Punitive vs.\nMonitoring&quot;)
pdf_width &lt;- 24
pdf_height &lt;- 11
layout_vec &lt;- c(0,  0, 23,  23, 23, 0,  24, 24, 24,
                0, 0, 17, 18, 19, 14, 20, 21, 22,
                25, 15, 1, 2, 3, 14, 4, 5, 6,
                0, 13, 13, 13, 13, 13, 13, 13, 13,
                26, 16, 7, 8, 9, 14, 10, 11, 12)
png_width &lt;- 2400
png_height &lt;- 1100

## MAKE PDF FIGURES FOR PAPER AND PNG FIGURES FOR RMD FILE

## CALLBACKS
pdf(&quot;out_figure1a_6x6_cb.pdf&quot;, width=pdf_width, height=pdf_height)
par(oma=c(5,0,0,0), mar=c(5,0,0,0))
layout(matrix(layout_vec, nrow=5, ncol=9, byrow=TRUE), widths=width_spec, heights=height_spec)
# plots
xlims &lt;- list(c(0, .25), c(-.15, .15), c(-.15, .15), c(-.2, .2))
itt.plot(cb, cb.reg, xlims)
# dividers
frame(); abline(h=.5, lwd=2)
frame(); abline(v=.5, lwd=2)
# row labels
frame(); text(.6, .85, &quot;White&quot;, cex=rowsub.cex); text(.6, .5, &quot;Black&quot;, cex=rowsub.cex); text(.6, .15, &quot;Hisp.&quot;, cex=rowsub.cex)
frame(); text(.6, .85, &quot;W-B&quot;, cex=rowsub.cex); text(.6, .5, &quot;W-H&quot;, cex=rowsub.cex); text(.6, .15, &quot;B-H&quot;, cex=rowsub.cex)
# col labels
for(i in 1:6) {
  frame(); text(.5, .5, col_headings[i], cex=colhead.cex)
}
# meta col labels
frame(); text(.5, .5, &quot;Treatment Group Means&quot;, cex=colhead.cex)
frame(); text(.5, .5, &quot;Differences between Treatment Groups&quot;, cex=colhead.cex)
# meta row labels
frame(); text(.5, .5, &quot;Tester Race\nGroup Means&quot;, cex=colhead.cex, srt=90)
frame(); text(.5, .5, &quot;Intergroup\nDifferences&quot;, cex=colhead.cex, srt=90)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code># Make PNG version for Rmd output
png(&quot;out_figure1a_6x6_cb.png&quot;, width=png_width, height=png_height)
par(oma=c(5,0,0,0), mar=c(5,0,0,0))
layout(matrix(layout_vec, nrow=5, ncol=9, byrow=TRUE), widths=width_spec, heights=height_spec)
# plots
xlims &lt;- list(c(0, .25), c(-.15, .15), c(-.15, .15), c(-.2, .2))
itt.plot(cb, cb.reg, xlims)
# dividers
frame(); abline(h=.5, lwd=2)
frame(); abline(v=.5, lwd=2)
# row labels
frame(); text(.6, .85, &quot;White&quot;, cex=rowsub.cex); text(.6, .5, &quot;Black&quot;, cex=rowsub.cex); text(.6, .15, &quot;Hisp.&quot;, cex=rowsub.cex)
frame(); text(.6, .85, &quot;W-B&quot;, cex=rowsub.cex); text(.6, .5, &quot;W-H&quot;, cex=rowsub.cex); text(.6, .15, &quot;B-H&quot;, cex=rowsub.cex)
# col labels
for(i in 1:6) {
  frame(); text(.5, .5, col_headings[i], cex=colhead.cex)
}
# meta col labels
frame(); text(.5, .5, &quot;Treatment Group Means&quot;, cex=colhead.cex)
frame(); text(.5, .5, &quot;Differences between Treatment Groups&quot;, cex=colhead.cex)
# meta row labels
frame(); text(.5, .5, &quot;Tester Race\nGroup Means&quot;, cex=colhead.cex, srt=90)
frame(); text(.5, .5, &quot;Intergroup\nDifferences&quot;, cex=colhead.cex, srt=90)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code>## OFFERS
pdf(&quot;out_figure1b_6x6_off.pdf&quot;, width=pdf_width, height=pdf_height)
par(oma=c(5,0,0,0), mar=c(5,0,0,0))
layout(matrix(layout_vec, nrow=5, ncol=9, byrow=TRUE), widths=width_spec, heights=height_spec)
# plots
xlims &lt;- list(c(0, .15), c(-.1, .1), c(-.1, .1), c(-.2, .2))
itt.plot(off, off.reg, xlims)
# dividers
frame(); abline(h=.5, lwd=2)
frame(); abline(v=.5, lwd=2)
# row labels
frame(); text(.6, .85, &quot;White&quot;, cex=rowsub.cex); text(.6, .5, &quot;Black&quot;, cex=rowsub.cex); text(.6, .15, &quot;Hisp.&quot;, cex=rowsub.cex)
frame(); text(.6, .85, &quot;W-B&quot;, cex=rowsub.cex); text(.6, .5, &quot;W-H&quot;, cex=rowsub.cex); text(.6, .15, &quot;B-H&quot;, cex=rowsub.cex)
# col labels
for(i in 1:6) {
  frame(); text(.5, .5, col_headings[i], cex=colhead.cex)
}
# meta col labels
frame(); text(.5, .5, &quot;Treatment Group Means&quot;, cex=colhead.cex)
frame(); text(.5, .5, &quot;Differences between Treatment Groups&quot;, cex=colhead.cex)
# meta row labels
frame(); text(.5, .5, &quot;Tester Race\nGroup Means&quot;, cex=colhead.cex, srt=90)
frame(); text(.5, .5, &quot;Intergroup\nDifferences&quot;, cex=colhead.cex, srt=90)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code># Make PNG version for Rmd output
png(&quot;out_figure1b_6x6_off.png&quot;, width=png_width, height=png_height)
par(oma=c(5,0,0,0), mar=c(5,0,0,0))
layout(matrix(layout_vec, nrow=5, ncol=9, byrow=TRUE), widths=width_spec, heights=height_spec)
# plots
xlims &lt;- list(c(0, .15), c(-.1, .1), c(-.1, .1), c(-.2, .2))
itt.plot(off, off.reg, xlims)
# dividers
frame(); abline(h=.5, lwd=2)
frame(); abline(v=.5, lwd=2)
# row labels
frame(); text(.6, .85, &quot;White&quot;, cex=rowsub.cex); text(.6, .5, &quot;Black&quot;, cex=rowsub.cex); text(.6, .15, &quot;Hisp.&quot;, cex=rowsub.cex)
frame(); text(.6, .85, &quot;W-B&quot;, cex=rowsub.cex); text(.6, .5, &quot;W-H&quot;, cex=rowsub.cex); text(.6, .15, &quot;B-H&quot;, cex=rowsub.cex)
# col labels
for(i in 1:6) {
  frame(); text(.5, .5, col_headings[i], cex=colhead.cex)
}
# meta col labels
frame(); text(.5, .5, &quot;Treatment Group Means&quot;, cex=colhead.cex)
frame(); text(.5, .5, &quot;Differences between Treatment Groups&quot;, cex=colhead.cex)
# meta row labels
frame(); text(.5, .5, &quot;Tester Race\nGroup Means&quot;, cex=colhead.cex, srt=90)
frame(); text(.5, .5, &quot;Intergroup\nDifferences&quot;, cex=colhead.cex, srt=90)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code>## INDEX
pdf(&quot;out_figurea3_6x6_index.pdf&quot;, width=pdf_width, height=pdf_height)
par(oma=c(5,0,0,0), mar=c(5,0,0,0))
layout(matrix(layout_vec, nrow=5, ncol=9, byrow=TRUE), widths=width_spec, heights=height_spec)
# plots
xlims &lt;- list(c(-.3, .3), c(-.35, .35), c(-.35, .35), c(-.35, .35))
itt.plot(meindex, meindex.reg, xlims)
# dividers
frame(); abline(h=.5, lwd=2)
frame(); abline(v=.5, lwd=2)
# row labels
frame(); text(.6, .85, &quot;White&quot;, cex=rowsub.cex); text(.6, .5, &quot;Black&quot;, cex=rowsub.cex); text(.6, .15, &quot;Hisp.&quot;, cex=rowsub.cex)
frame(); text(.6, .85, &quot;W-B&quot;, cex=rowsub.cex); text(.6, .5, &quot;W-H&quot;, cex=rowsub.cex); text(.6, .15, &quot;B-H&quot;, cex=rowsub.cex)
# col labels
for(i in 1:6) {
  frame(); text(.5, .5, col_headings[i], cex=colhead.cex)
}
# meta col labels
frame(); text(.5, .5, &quot;Treatment Group Means&quot;, cex=colhead.cex)
frame(); text(.5, .5, &quot;Differences between Treatment Groups&quot;, cex=colhead.cex)
# meta row labels
frame(); text(.5, .5, &quot;Tester Race\nGroup Means&quot;, cex=colhead.cex, srt=90)
frame(); text(.5, .5, &quot;Intergroup\nDifferences&quot;, cex=colhead.cex, srt=90)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code># Make PNG version for Rmd output
png(&quot;out_figurea3_6x6_index.png&quot;, width=2400, height=1100)
par(oma=c(5,0,0,0), mar=c(5,0,0,0))
layout(matrix(layout_vec, nrow=5, ncol=9, byrow=TRUE), widths=width_spec, heights=height_spec)
# plots
xlims &lt;- list(c(-.3, .3), c(-.35, .35), c(-.35, .35), c(-.35, .35))
itt.plot(meindex, meindex.reg, xlims)
# dividers
frame(); abline(h=.5, lwd=2)
frame(); abline(v=.5, lwd=2)
# row labels
frame(); text(.6, .85, &quot;White&quot;, cex=rowsub.cex); text(.6, .5, &quot;Black&quot;, cex=rowsub.cex); text(.6, .15, &quot;Hisp.&quot;, cex=rowsub.cex)
frame(); text(.6, .85, &quot;W-B&quot;, cex=rowsub.cex); text(.6, .5, &quot;W-H&quot;, cex=rowsub.cex); text(.6, .15, &quot;B-H&quot;, cex=rowsub.cex)
# col labels
for(i in 1:6) {
  frame(); text(.5, .5, col_headings[i], cex=colhead.cex)
}
# meta col labels
frame(); text(.5, .5, &quot;Treatment Group Means&quot;, cex=colhead.cex)
frame(); text(.5, .5, &quot;Differences between Treatment Groups&quot;, cex=colhead.cex)
# meta row labels
frame(); text(.5, .5, &quot;Tester Race\nGroup Means&quot;, cex=colhead.cex, srt=90)
frame(); text(.5, .5, &quot;Intergroup\nDifferences&quot;, cex=colhead.cex, srt=90)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Figure 1, Panel A: Outcome 1: Net Discrimination in Receiving Callbacks" />
<p class="caption"><strong>Figure 1, Panel A: Outcome 1: Net Discrimination in Receiving Callbacks</strong></p>
</div>
<div class="figure">
<img 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" alt="Figure 1, Panel B: Outcome 2: Net Discrimination in Receiving Offers" />
<p class="caption"><strong>Figure 1, Panel B: Outcome 2: Net Discrimination in Receiving Offers</strong></p>
</div>
</div>
</div>
<div id="figure-2-posterior-densities-of-treatment-effects-on-discrimination" class="section level2">
<h2><span class="header-section-number">1.2</span> Figure 2: Posterior Densities of Treatment Effects on Discrimination</h2>
<pre class="r"><code>##=============================================================================##
## Figure 2: Posterior Probabilities
##=============================================================================##

#### ======== FUNCTIONS =========== ####

## function to compute the bayesian analog of the lmfit
## using non-informative priors and Monte Carlo scheme
## based on N samples;
#  see http://www.r-bloggers.com/bayesian-linear-regression-analysis-without-tears-r/
bayesfit&lt;-function(lmfit,N){
  QR&lt;-lmfit$qr
  df.residual&lt;-lmfit$df.residual
  R&lt;-qr.R(QR) ## R component
  coef&lt;-lmfit$coef
  Vb&lt;-chol2inv(R) ## variance(unscaled)
  s2&lt;-(t(lmfit$residuals)%*%lmfit$residuals)
  s2&lt;-s2[1,1]/df.residual
  
  ## now to sample residual variance
  sigma&lt;-df.residual*s2/rchisq(N,df.residual)
  coef.sim&lt;-sapply(sigma,function(x) mvrnorm(1,coef,Vb*x))
  ret&lt;-data.frame(t(coef.sim))
  names(ret)&lt;-names(lmfit$coef)
  ret$sigma&lt;-sqrt(sigma)
  ret
}

Bayes.sum&lt;-function(x)
{
  c(&quot;mean&quot;=mean(x),
    &quot;se&quot;=sd(x),
    &quot;t&quot;=mean(x)/sd(x),
    &quot;median&quot;=median(x),
    &quot;CrI&quot;=quantile(x,prob=0.025),
    &quot;CrI&quot;=quantile(x,prob=0.975)
  )
}

bayes.graph &lt;- function(depvar, treat, weight, block = NULL, main = &quot;Callbacks&quot;, #comparison = &quot;Hispanic&quot;, 
                       trt_text = &quot;Punitive&quot;, draws = 10000, xloc = 0.15, yloc = 13, textadj = 1, ylab = &quot;&quot;, print_title = FALSE,
                       vertical = TRUE, maxline = FALSE, addlegend = FALSE, addbox = TRUE, ylim = c(0,18)) {

  lmfit &lt;- lm(depvar ~ treat + factor(block), weights = weight)
  bf &lt;- bayesfit(lmfit, draws)
  t(apply(bf,2,Bayes.sum))
  summary(lmfit)
  post_punitive &lt;- bf$treat

  # graph
  dens &lt;- density(post_punitive, bw = 0.01, from = -.17, to = .10) # to = .04 ; bw = 0.005

  left &lt;- quantile(post_punitive, 0)
  x1 &lt;- min(which(dens$x &gt;= left))
  x2 &lt;- max(which(dens$x &lt;  0))

  pct_left &lt;- round((diff(dens$x[1:2])*sum(dens$y[1:x2])) / (diff(dens$x[1:2])*sum(dens$y)), digits = 3) * 100 # % MOC &lt; 0

  main_txt &lt;- &quot;&quot;
  if(print_title) { main_txt &lt;- paste0(&quot;Outcome: &quot;, main, &quot;\n(&quot;, trt_text, &quot; vs. Control)&quot;) }
  plot(dens, xlab = &quot;&quot;, ylab = ylab, main = main_txt, bty = &quot;n&quot;, lwd = 2, xlim = c(-.15, .15), ylim = ylim, cex.main = 1.5, cex.lab = 1.2, cex.axis = 1, font = 1) #xlim = c(-.17, .10), ylim = c(0,17)
  with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col=&quot;gray&quot;))
  if(maxline) abline(v = mean(post_punitive), col = &quot;red&quot;, lty = 2, lwd = 3)
  text(x = xloc, y = yloc, paste0(pct_left, &quot;%&quot;), adj = c(textadj, NA), cex = 1.2)
  text(x = xloc, y = yloc-.8, &quot;less than zero&quot;, adj = c(textadj, NA), cex = 1.2)
  if(vertical) abline(v=0)
  if(addbox) box()
}


#### ======== ANALYSES AND GRAPH RESULTS =========== ####

# CREATE PDF OUTPUT FOR MANUSCRIPT

op &lt;- par()
pdf(&quot;out_figure2_posteriors.pdf&quot;, width = 13.5, height = 7.5)
par(mfrow = c(2,4), mar=c(2.5,5,5,0.5), family=&quot;Helvetica&quot;) 
  
  # Hispanic
  # callback
  bayes.graph(dat$ncb_wh[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], trt_text = &quot;Monitoring&quot;, ylab = &quot;Discrimination against Hispanics (vs. Whites)&quot;, print_title = TRUE)
  bayes.graph(dat$ncb_wh[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], print_title = TRUE)

    # offer
  bayes.graph(dat$noff_wh[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], main = &quot;Offers&quot;, trt_text = &quot;Monitoring&quot;, print_title = TRUE)
  bayes.graph(dat$noff_wh[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], main = &quot;Offers&quot;, print_title = TRUE)

  # Black
  # callback
  bayes.graph(dat$ncb_wb[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], trt_text = &quot;Monitoring&quot;, xloc = -0.15, textadj = 0, ylab = &quot;Discrimination against Blacks (vs. Whites)&quot;)
  bayes.graph(dat$ncb_wb[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], xloc = -0.15, textadj = 0)
  
  # offer
  bayes.graph(dat$noff_wb[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], main = &quot;Offers&quot;, trt_text = &quot;Monitoring&quot;, xloc = -0.15, textadj = 0)
  bayes.graph(dat$noff_wb[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], main = &quot;Offers&quot;, xloc = -0.15, textadj = 0)
  
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code># CREATE PNG OUTPUT FOR RMD OUTPUT

par(op)
png(&quot;out_figure2_posteriors.png&quot;, width = 1350, height = 750)
par(mfrow = c(2,4), mar=c(2.5,5,5,0.5), family=&quot;Helvetica&quot;)

# Hispanic
# callback
bayes.graph(dat$ncb_wh[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], trt_text = &quot;Monitoring&quot;, ylab = &quot;Discrimination against Hispanics (vs. whites)&quot;, print_title = TRUE)
bayes.graph(dat$ncb_wh[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], print_title = TRUE)


# offer
bayes.graph(dat$noff_wh[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], main = &quot;Offers&quot;, trt_text = &quot;Monitoring&quot;, print_title = TRUE)
bayes.graph(dat$noff_wh[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], main = &quot;Offers&quot;, print_title = TRUE)

# Black
# callback
bayes.graph(dat$ncb_wb[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], trt_text = &quot;Monitoring&quot;, xloc = -0.15, textadj = 0, ylab = &quot;Discrimination against Blacks (vs. whites)&quot;)
bayes.graph(dat$ncb_wb[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], xloc = -0.15, textadj = 0)

# offer
bayes.graph(dat$noff_wb[dat$TA!=2], (dat$TA==1)[dat$TA!=2], dat$ipw[dat$TA!=2], dat$block[dat$TA!=2], main = &quot;Offers&quot;, trt_text = &quot;Monitoring&quot;, xloc = -0.15, textadj = 0)
bayes.graph(dat$noff_wb[dat$TA!=1], (dat$TA==2)[dat$TA!=1], dat$ipw[dat$TA!=1], dat$block[dat$TA!=1], main = &quot;Offers&quot;, xloc = -0.15, textadj = 0)

dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Figure 2: Posterior Densities of Treatment Effects on Discrimination against Hispanics (top) and African Americans (bottom) Relative to Whites" />
<p class="caption"><strong>Figure 2: Posterior Densities of Treatment Effects on Discrimination against Hispanics (top) and African Americans (bottom) Relative to Whites</strong></p>
</div>
</div>
<div id="randomization-check-reported-in-main-text-footnote-17" class="section level2">
<h2><span class="header-section-number">1.3</span> Randomization Check (reported in main text, footnote 17)</h2>
<pre class="r"><code>set.seed(1234567)

Xs = c(&quot;frame&quot;, &quot;partnered&quot;, &quot;rent + m.rent&quot;, &quot;numbr&quot;, &quot;sqft + m.sqft&quot;,&quot;regime1&quot;,&quot;regime2&quot;,&quot;regime3&quot;,
       &quot;nnumattr_bh&quot;, &quot;nnumattr_wh&quot;, &quot;nnumattr_wb&quot;, &quot;nnumskep_bh&quot;, &quot;nnumskep_wh&quot;, &quot;nnumskep_wb&quot;, 
       &quot;npctskep_bh&quot;, &quot;npctskep_wh&quot;, &quot;npctskep_wb&quot;, &quot;nnumpos_bh&quot; , &quot;nnumpos_wh&quot; , &quot;nnumpos_wb&quot; , 
       &quot;nnumneu_bh&quot; , &quot;nnumneu_wh&quot; , &quot;nnumneu_wb&quot; , &quot;nnumneg_bh&quot; , &quot;nnumneg_wh&quot; , &quot;nnumneg_wb&quot; , 
       &quot;npctpos_bh&quot; , &quot;npctpos_wh&quot; , &quot;npctpos_wb&quot; , &quot;npctneu_bh&quot; , &quot;npctneu_wh&quot; , &quot;npctneu_wb&quot; , 
       &quot;npctneg_bh&quot; , &quot;npctneg_wh&quot; , &quot;npctneg_wb&quot; , &quot;nanyskep_bh&quot;, &quot;nanyskep_wh&quot;, &quot;nanyskep_wb&quot;, 
       &quot;nanyneg_bh&quot; , &quot;nanyneg_wh&quot; , &quot;nanyneg_wb&quot; , &quot;team_gender&quot;, &quot;broker&quot;,
       &quot;callorder.wb&quot;, &quot;callorder.wh&quot;, &quot;callorder.bh&quot;,
       &quot;incrank.wb.gt&quot;, &quot;incrank.wh.gt&quot;, &quot;incrank.bh.gt&quot;,
       &quot;incrank.wb.eq&quot;, &quot;incrank.wh.eq&quot;, &quot;incrank.bh.eq&quot;,
       &quot;incrank.wb.lt&quot;, &quot;incrank.wh.lt&quot;, &quot;incrank.bh.lt&quot;,
       &quot;boro.brx&quot;, &quot;boro.brk&quot;, &quot;boro.mnh&quot;, &quot;boro.que&quot;, &quot;boro.stn&quot;,
       &quot;inc.w.hi&quot;,&quot;inc.b.hi&quot;,&quot;inc.h.hi&quot;,&quot;ll.female + m.ll.female&quot;,
       llrace, llage, testerfe)
fmla = as.formula(paste0(&quot;TA ~ &quot;, paste(Xs, collapse=&quot;+&quot;), &quot;+ as.factor(block)&quot;))
mnl_fit = multinom(formula=fmla, data=dat, weight=ipw, trace=FALSE) # trace=FALSE to suppress output about convergence
kparams = length(mnl_fit$coefnames)
mnl_loglik = -(mnl_fit$AIC - 2*kparams)/2   # log likelihood from actual model

# randomization inference
ri_mat = cbind(1:nrow(dat), dat$block, dat$TA)
blocks = names(table(dat$block))

# recalculate loglik stat for each shuffled vector of assignments
niter = 1000
mnl_loglik_ri = rep(NA, niter)
fmla_ri = as.formula(paste0(&quot;z_ri ~ &quot;, paste(Xs, collapse=&quot;+&quot;), &quot;+ as.factor(block)&quot;))
for(j in 1:niter){
  shuffle_mat = list()
  for(i in 1:length(blocks)){
    block_z = ri_mat[ri_mat[,2] == blocks[i],3]
    shuffle_z = sample(block_z, length(block_z), replace=FALSE)
    shuffle_mat[[i]] = cbind(ri_mat[ri_mat[,2] == blocks[i],1:2], shuffle_z)
  }
  shuffle_mat = do.call(rbind, shuffle_mat)
  shuffle_mat = shuffle_mat[order(shuffle_mat[,1]),]
  dat$z_ri = shuffle_mat[,3]
  mnl_fit_ri = multinom(formula=fmla_ri, data=dat, weight=ipw, trace=FALSE)
  kparams = length(mnl_fit_ri$coefnames)
  mnl_loglik_ri[j] = -(mnl_fit_ri$AIC - 2*kparams)/2
}

# calculate p-value
mean(abs(mnl_loglik_ri) &gt;= abs(mnl_loglik))</code></pre>
<pre><code>## [1] 0.97</code></pre>
</div>
</div>
<div id="appendix-tables-and-figures" class="section level1">
<h1><span class="header-section-number">2</span> Appendix Tables and Figures</h1>
<div id="table-a1-p.a-2-distribution-of-experimental-subjects-by-randomization-block" class="section level2">
<h2><span class="header-section-number">2.1</span> Table A1 (p. A-2): Distribution of Experimental Subjects by Randomization Block</h2>
<pre class="r"><code>blockLabs &lt;- rep(c(&quot;Brooklyn&quot;,&quot;Bronx&quot;,&quot;Manhattan&quot;,&quot;Queens&quot;,&quot;Staten Island&quot;,&quot;Likely Discrimination Frame&quot;), 3)
blockLabs &lt;- blockLabs[-length(blockLabs)]
pcts &lt;- round(prop.table(table(dat$block, dat$TA, useNA=&quot;ifany&quot;)), 3)
block_dist &lt;- cbind(blockLabs, 
                    table(dat$block, dat$TA, useNA=&quot;ifany&quot;)[,1], pcts[,1],
                    table(dat$block, dat$TA, useNA=&quot;ifany&quot;)[,2], pcts[,2],
                    table(dat$block, dat$TA, useNA=&quot;ifany&quot;)[,3], pcts[,3])

block_dist &lt;- rbind(c(&quot;Regime 1: 13 Apr 2012 - 9 Sep 2012&quot;, rep(NA,6)),
                    block_dist[1:6,],
                    c(&quot;Regime 2: 10 Sep 2012 - 7 May 7, 2013&quot;, rep(NA,6)),
                    block_dist[7:12,],
                    c(&quot;Regime 3: 8 May 2013 to 20 Dec 2013&quot;, rep(NA,6)),
                    block_dist[13:17,],
                    c(&quot;Total&quot;, table(dat$TA)[1], round(table(dat$TA)[1]/sum(table(dat$TA)),3), 
                      table(dat$TA)[2], round(table(dat$TA)[2]/sum(table(dat$TA)),3), 
                      table(dat$TA)[3], round(table(dat$TA)[3]/sum(table(dat$TA)),3)
                    ))

colnames(block_dist) &lt;- c(&quot;Block&quot;,&quot;Control (N)&quot;,&quot;Control (Proportion)&quot;,
                  &quot;Monitoring (N)&quot;,&quot;Monitoring (Proportion)&quot;,
                  &quot;Punitive (N)&quot;,&quot;Punitive (Proportion)&quot;)

block_distcaption = &quot;Distribution of Experimental Subjects by Randomization Block. Cells contain counts of the number and proportion of experimental subjects randomly assigned to each arm by randomization block. The 17 randomization blocks are defined by the ad's sampling stratum (New York City borough or the likely discrimination, or LD, oversample) and by treatment regime (defined as a distinct design and randomization procedure). There are three treatment regimes. Regime 1 was a 5-arm design (2 of which are not analyzed in this paper) with equal treatment assignment probabilities. Regime 2 was a 3-arm design where the probability of assignment to control was 0.5 and the probability of assignment to the monitoring or punitive conditions was 0.25. Regime 3 was a 3-arm design with equal treatment assignment probabilities. Proportions may not sum to 1 due to rounding.&quot;

# export table to tex file
print(xtable(block_dist, caption = block_distcaption, label= &quot;blockNs&quot;), file=&quot;out_table_a1_dist_by_block.tex&quot;, hline.after=c(0,7,14,20,21), include.rownames=FALSE,  only.contents=TRUE)

# show table in Rmd output file
rownames(block_dist) &lt;- NULL
kable(block_dist, caption=&quot;**Table A1**&quot;)</code></pre>
<table>
<caption><strong>Table A1</strong></caption>
<thead>
<tr class="header">
<th align="left">Block</th>
<th align="left">Control (N)</th>
<th align="left">Control (Proportion)</th>
<th align="left">Monitoring (N)</th>
<th align="left">Monitoring (Proportion)</th>
<th align="left">Punitive (N)</th>
<th align="left">Punitive (Proportion)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Regime 1: 13 Apr 2012 - 9 Sep 2012</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">14</td>
<td align="left">0.021</td>
<td align="left">11</td>
<td align="left">0.017</td>
<td align="left">17</td>
<td align="left">0.026</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">4</td>
<td align="left">0.006</td>
<td align="left">3</td>
<td align="left">0.005</td>
<td align="left">2</td>
<td align="left">0.003</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">13</td>
<td align="left">0.02</td>
<td align="left">12</td>
<td align="left">0.018</td>
<td align="left">18</td>
<td align="left">0.028</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">2</td>
<td align="left">0.003</td>
<td align="left">3</td>
<td align="left">0.005</td>
<td align="left">6</td>
<td align="left">0.009</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">1</td>
<td align="left">0.002</td>
<td align="left">1</td>
<td align="left">0.002</td>
<td align="left">0</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">8</td>
<td align="left">0.012</td>
<td align="left">8</td>
<td align="left">0.012</td>
<td align="left">9</td>
<td align="left">0.014</td>
</tr>
<tr class="even">
<td align="left">Regime 2: 10 Sep 2012 - 7 May 7, 2013</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Brooklyn</td>
<td align="left">63</td>
<td align="left">0.096</td>
<td align="left">24</td>
<td align="left">0.037</td>
<td align="left">29</td>
<td align="left">0.044</td>
</tr>
<tr class="even">
<td align="left">Bronx</td>
<td align="left">21</td>
<td align="left">0.032</td>
<td align="left">10</td>
<td align="left">0.015</td>
<td align="left">11</td>
<td align="left">0.017</td>
</tr>
<tr class="odd">
<td align="left">Manhattan</td>
<td align="left">50</td>
<td align="left">0.077</td>
<td align="left">32</td>
<td align="left">0.049</td>
<td align="left">23</td>
<td align="left">0.035</td>
</tr>
<tr class="even">
<td align="left">Queens</td>
<td align="left">28</td>
<td align="left">0.043</td>
<td align="left">10</td>
<td align="left">0.015</td>
<td align="left">14</td>
<td align="left">0.021</td>
</tr>
<tr class="odd">
<td align="left">Staten Island</td>
<td align="left">8</td>
<td align="left">0.012</td>
<td align="left">2</td>
<td align="left">0.003</td>
<td align="left">3</td>
<td align="left">0.005</td>
</tr>
<tr class="even">
<td align="left">Likely Discrimination Frame</td>
<td align="left">11</td>
<td align="left">0.017</td>
<td align="left">4</td>
<td align="left">0.006</td>
<td align="left">4</td>
<td align="left">0.006</td>
</tr>
<tr class="odd">
<td align="left">Regime 3: 8 May 2013 to 20 Dec 2013</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">13</td>
<td align="left">0.02</td>
<td align="left">23</td>
<td align="left">0.035</td>
<td align="left">14</td>
<td align="left">0.021</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">6</td>
<td align="left">0.009</td>
<td align="left">3</td>
<td align="left">0.005</td>
<td align="left">8</td>
<td align="left">0.012</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">25</td>
<td align="left">0.038</td>
<td align="left">18</td>
<td align="left">0.028</td>
<td align="left">25</td>
<td align="left">0.038</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">8</td>
<td align="left">0.012</td>
<td align="left">7</td>
<td align="left">0.011</td>
<td align="left">14</td>
<td align="left">0.021</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">4</td>
<td align="left">0.006</td>
<td align="left">3</td>
<td align="left">0.005</td>
<td align="left">3</td>
<td align="left">0.005</td>
</tr>
<tr class="odd">
<td align="left">Total</td>
<td align="left">279</td>
<td align="left">0.427</td>
<td align="left">174</td>
<td align="left">0.266</td>
<td align="left">200</td>
<td align="left">0.306</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a2-p.a-3-incidence-of-early-stage-discrimination" class="section level2">
<h2><span class="header-section-number">2.2</span> Table A2 (p. A-3): Incidence of Early Stage Discrimination</h2>
<pre class="r"><code># create table shells

wb &lt;- matrix(NA, nrow=length(es.a), ncol=16)
wh &lt;- matrix(NA, nrow=length(es.a), ncol=16)
bh &lt;- matrix(NA, nrow=length(es.a), ncol=16)

# populate column headings
colnames(wb) &lt;- c(&quot;Measure&quot;, paste( c(rep(&quot;AuditSamp-&quot;,5),rep(&quot;ExpSamp-&quot;,5),rep(&quot;Control-&quot;,5)) , rep(c(&quot;Maj-Mean&quot;,&quot;Min-Mean&quot;,&quot;Diff&quot;,&quot;P&quot;,&quot;N&quot;),3), sep=&quot;&quot;))
colnames(wh) &lt;- c(&quot;Measure&quot;, paste( c(rep(&quot;AuditSamp-&quot;,5),rep(&quot;ExpSamp-&quot;,5),rep(&quot;Control-&quot;,5)) , rep(c(&quot;Maj-Mean&quot;,&quot;Min-Mean&quot;,&quot;Diff&quot;,&quot;P&quot;,&quot;N&quot;),3), sep=&quot;&quot;))
colnames(bh) &lt;- c(&quot;Measure&quot;, paste( c(rep(&quot;AuditSamp-&quot;,5),rep(&quot;ExpSamp-&quot;,5),rep(&quot;Control-&quot;,5)) , rep(c(&quot;Maj-Mean&quot;,&quot;Min-Mean&quot;,&quot;Diff&quot;,&quot;P&quot;,&quot;N&quot;),3), sep=&quot;&quot;))

# populate first column - variable names
wb[,1] &lt;- c(es.wb.labs)
wh[,1] &lt;- c(es.wh.labs)
bh[,1] &lt;- c(es.bh.labs)

# check variables
# for(i in 1:length(es.a)){
#   cat(&quot;\n *********** variable : &quot;, es.a.labs[i], &quot;****************\n&quot;, sep=&quot;&quot;)
#   print(table(aud[[es.a[i]]], useNA=&quot;ifany&quot;))
#   cat(&quot;\n *********** variable : &quot;, es.b.labs[i], &quot;****************\n&quot;, sep=&quot;&quot;)
#   print(table(aud[[es.b[i]]], useNA=&quot;ifany&quot;))
#   cat(&quot;\n *********** variable : &quot;, es.c.labs[i], &quot;****************\n&quot;, sep=&quot;&quot;)
#   print(table(aud[[es.c[i]]], useNA=&quot;ifany&quot;))
# }

# convert all vars into numeric

cols &lt;- c(es.a, es.b, es.c, es.wb, es.wh, es.bh)
aud[,cols] &lt;- lapply(aud[,cols], as.numeric)

# populate table shells

for(i in 1:nrow(wb)){
  # audit sample
  wb[i,2] &lt;- mean(aud[[es.c[i]]])
  wb[i,3] &lt;- mean(aud[[es.a[i]]])
  wb[i,4] &lt;- as.numeric(wb[i,2]) - as.numeric(wb[i,3])
  wb[i,5] &lt;- t.test(x=aud[[es.c[i]]], y=aud[[es.a[i]]], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  wb[i,6] &lt;- length(aud[[es.c[i]]]) 
  
  # experiment sample
  wb[i,7] &lt;- mean(aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ])
  wb[i,8] &lt;- mean(aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ])
  wb[i,9] &lt;- as.numeric(wb[i,7]) - as.numeric(wb[i,8])
  wb[i,10] &lt;- t.test(x=aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ], y=aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  wb[i,11] &lt;- length(aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ]) 
  
  # control group
  wb[i,12] &lt;- mean(aud[[es.c[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0])
  wb[i,13] &lt;- mean(aud[[es.a[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0])
  wb[i,14] &lt;- as.numeric(wb[i,12]) - as.numeric(wb[i,13])
  wb[i,15] &lt;- t.test(x=aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) &amp; aud$TA==0], y=aud[[es.a[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  wb[i,16] &lt;- length(aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp)  &amp; aud$TA==0]) 
  
}


for(i in 1:nrow(wh)){
  # audit sample
  wh[i,2] &lt;- mean(aud[[es.c[i]]])
  wh[i,3] &lt;- mean(aud[[es.b[i]]])
  wh[i,4] &lt;- as.numeric(wh[i,2]) - as.numeric(wh[i,3])
  wh[i,5] &lt;- t.test(x=aud[[es.c[i]]], y=aud[[es.b[i]]], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  wh[i,6] &lt;- length(aud[[es.c[i]]]) 
  
  # experiment sample
  wh[i,7] &lt;- mean(aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ])
  wh[i,8] &lt;- mean(aud[[es.b[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ])
  wh[i,9] &lt;- as.numeric(wh[i,7]) - as.numeric(wh[i,8])
  wh[i,10] &lt;- t.test(x=aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ], y=aud[[es.b[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  wh[i,11] &lt;- length(aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ]) 
  
  # control group
  wh[i,12] &lt;- mean(aud[[es.c[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0])
  wh[i,13] &lt;- mean(aud[[es.b[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0])
  wh[i,14] &lt;- as.numeric(wh[i,12]) - as.numeric(wh[i,13])
  wh[i,15] &lt;- t.test(x=aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) &amp; aud$TA==0], y=aud[[es.b[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  wh[i,16] &lt;- length(aud[[es.c[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp)  &amp; aud$TA==0]) 
  
}

for(i in 1:nrow(bh)){
  # audit sample
  bh[i,2] &lt;- mean(aud[[es.a[i]]])
  bh[i,3] &lt;- mean(aud[[es.b[i]]])
  bh[i,4] &lt;- as.numeric(bh[i,2]) - as.numeric(bh[i,3])
  bh[i,5] &lt;- t.test(x=aud[[es.a[i]]], y=aud[[es.b[i]]], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  bh[i,6] &lt;- length(aud[[es.a[i]]]) 
  
  # experiment sample
  bh[i,7] &lt;- mean(aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ])
  bh[i,8] &lt;- mean(aud[[es.b[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ])
  bh[i,9] &lt;- as.numeric(bh[i,7]) - as.numeric(bh[i,8])
  bh[i,10] &lt;- t.test(x=aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ], y=aud[[es.b[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  bh[i,11] &lt;- length(aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) ]) 
  
  # control group
  bh[i,12] &lt;- mean(aud[[es.a[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0])
  bh[i,13] &lt;- mean(aud[[es.b[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0])
  bh[i,14] &lt;- as.numeric(bh[i,12]) - as.numeric(bh[i,13])
  bh[i,15] &lt;- t.test(x=aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp) &amp; aud$TA==0], y=aud[[es.b[i]]][aud$insamp==&quot;1&quot;  &amp; !is.na(aud$insamp) &amp; aud$TA==0], alternative=&quot;two.sided&quot;, paired=TRUE, conf.level=0.95)$p.value
  bh[i,16] &lt;- length(aud[[es.a[i]]][aud$insamp==&quot;1&quot; &amp; !is.na(aud$insamp)  &amp; aud$TA==0]) 
  
}

for(i in 2:ncol(wb)){
  wb[,i] &lt;- round(as.numeric(wb[,i]), 3)
  wh[,i] &lt;- round(as.numeric(wh[,i]), 3)
  bh[,i] &lt;- round(as.numeric(bh[,i]), 3)
  
  if(i %in% c(5,10,15)) {
    wb[,i] &lt;- paste(&quot;(&quot;,wb[,i],&quot;)&quot;,sep=&quot;&quot;)
    wh[,i] &lt;- paste(&quot;(&quot;,wh[,i],&quot;)&quot;,sep=&quot;&quot;)
    bh[,i] &lt;- paste(&quot;(&quot;,bh[,i],&quot;)&quot;,sep=&quot;&quot;)
  }
  if(i %in% c(6,11,16)) {
    wb[,i] &lt;- paste(&quot;[&quot;,wb[,i],&quot;]&quot;,sep=&quot;&quot;)
    wh[,i] &lt;- paste(&quot;[&quot;,wh[,i],&quot;]&quot;,sep=&quot;&quot;)
    bh[,i] &lt;- paste(&quot;[&quot;,bh[,i],&quot;]&quot;,sep=&quot;&quot;)
  }
}


es_discrim_full_table &lt;- rbind(wb, wh, bh)

table_a2 &lt;- es_discrim_full_table[c(1,2,14,15,27,28),1:6]
colnames(table_a2)[2:6] &lt;- c(&quot;Majority Group Mean&quot;, &quot;Minority Group Mean&quot;, &quot;Difference (Maj-Min)&quot;, &quot;p-value&quot;, &quot;[N]&quot;)

print(xtable(table_a2, caption=c(&quot;Incidence of Early Stage Discrimination&quot;,
                            &quot;Incidence of Early Stage Discrimination&quot;)),
      file=&quot;out_tablea2_early_stage_discrim.tex&quot;,
      include.rownames=FALSE)

kable(table_a2, caption=&quot;**Table A2**&quot;)</code></pre>
<table>
<caption><strong>Table A2</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Any contact (White vs. Black)</td>
<td align="left">0.512</td>
<td align="left">0.524</td>
<td align="left">-0.012</td>
<td align="left">(0.184)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Scheduling appointment (White vs. Black)</td>
<td align="left">0.348</td>
<td align="left">0.361</td>
<td align="left">-0.013</td>
<td align="left">(0.035)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Any contact (White vs. Hispanic)</td>
<td align="left">0.512</td>
<td align="left">0.512</td>
<td align="left">0</td>
<td align="left">(0.968)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Scheduling appointment (White vs. Hispanic)</td>
<td align="left">0.348</td>
<td align="left">0.354</td>
<td align="left">-0.006</td>
<td align="left">(0.283)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Any contact (Black vs. Hispanic)</td>
<td align="left">0.524</td>
<td align="left">0.512</td>
<td align="left">0.012</td>
<td align="left">(0.189)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Scheduling appointment (Black vs. Hispanic)</td>
<td align="left">0.361</td>
<td align="left">0.354</td>
<td align="left">0.007</td>
<td align="left">(0.284)</td>
<td align="left">[2711]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#### F-test for the null hypothesis that race does not matter for making any contact or for scheduling appointment

aud.rs &lt;- melt(aud[,c(&quot;cid&quot;, &quot;contact_A&quot;, &quot;contact_B&quot;, &quot;contact_C&quot;, &quot;sched_A&quot;, &quot;sched_B&quot;, &quot;sched_C&quot;)], id.vars=&quot;cid&quot;)

aud.rs$ttype &lt;- sapply(strsplit(as.character(aud.rs$variable), &quot;_&quot;, fixed=TRUE), function(x) x[2])
aud.rs$variable &lt;- sapply(strsplit(as.character(aud.rs$variable), &quot;_&quot;, fixed=TRUE), function(x) x[1])
aud.rs &lt;- dcast(aud.rs, cid + ttype ~ variable, value.var=&quot;value&quot;)

fit.contact &lt;- lm(contact ~ ttype, data=aud.rs)
fit.sched &lt;- lm(sched ~ ttype, data=aud.rs)

print(&quot;F-test of the null that tester race predicts whether the tester makes any contact with the landlord&quot;)</code></pre>
<pre><code>## [1] &quot;F-test of the null that tester race predicts whether the tester makes any contact with the landlord&quot;</code></pre>
<pre class="r"><code>print(summary(fit.contact))</code></pre>
<pre><code>## 
## Call:
## lm(formula = contact ~ ttype, data = aud.rs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5242 -0.5124  0.4758  0.4876  0.4880 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.524161   0.009599  54.605   &lt;2e-16 ***
## ttypeB      -0.012173   0.013575  -0.897    0.370    
## ttypeC      -0.011804   0.013575  -0.870    0.385    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4998 on 8130 degrees of freedom
## Multiple R-squared:  0.000128,   Adjusted R-squared:  -0.000118 
## F-statistic: 0.5203 on 2 and 8130 DF,  p-value: 0.5944</code></pre>
<pre class="r"><code>print(&quot;F-test of the null that tester race predicts whether the tester successfully schedules an appointment&quot;)</code></pre>
<pre><code>## [1] &quot;F-test of the null that tester race predicts whether the tester successfully schedules an appointment&quot;</code></pre>
<pre class="r"><code>print(summary(fit.sched))</code></pre>
<pre><code>## 
## Call:
## lm(formula = sched ~ ttype, data = aud.rs)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3611 -0.3545 -0.3482  0.6389  0.6518 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(&gt;|t|)    
## (Intercept)  0.361121   0.009189  39.299   &lt;2e-16 ***
## ttypeB      -0.006640   0.012995  -0.511    0.609    
## ttypeC      -0.012910   0.012995  -0.993    0.321    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4785 on 8130 degrees of freedom
## Multiple R-squared:  0.0001214,  Adjusted R-squared:  -0.0001246 
## F-statistic: 0.4936 on 2 and 8130 DF,  p-value: 0.6104</code></pre>
</div>
<div id="figure-a2-p.a-5-cumulative-number-of-cases-over-implementation-period" class="section level2">
<h2><span class="header-section-number">2.3</span> Figure A2 (p. A-5): Cumulative Number of Cases Over Implementation Period</h2>
<pre class="r"><code>dat$casedate &lt;- as.Date(dat$casedate, format=&quot;%Y-%m-%d&quot;)
casedate &lt;- dat$casedate[order(dat$casedate)]

# generate .png
png(&quot;out_figurea2_cases_over_time.png&quot;, height=500, width=1200)
par(oma=c(2,0,3,0), xpd=TRUE)

plot(casedate, 1:length(casedate), xaxt=&quot;n&quot;, yaxt=&quot;n&quot;, type=&quot;l&quot;, ylim=c(0,700), xlab=&quot;&quot;, ylab=&quot;Cumulative Number of Cases&quot;, xaxs=&quot;i&quot;, yaxs=&quot;i&quot;)
axis.Date(side=1, at=seq(as.Date(&quot;2012-04-01&quot;), as.Date(&quot;2014-01-01&quot;), &quot;month&quot;), format=&quot;%m/%Y&quot;, las=2, cex.axis=0.9)
axis(side=2, at=seq(0,700,100), labels=seq(0,700,100), cex.axis=0.9, las=1)
polygon(x=c(rep(as.Date(&quot;2012-10-29&quot;),2), rep(as.Date(&quot;2012-11-11&quot;),2)),
        y=c(700,0,0,700),
        col=rgb(.8,.8,.8,.25),
        border=rgb(.8,.8,.8,.25))
text(x=as.Date(&quot;2012-11-11&quot;), y=600, &quot;Implementation Halted \nDue to Hurricane Sandy&quot;, cex=.9, pos=1)
mtext(side=1, &quot;Date&quot;, outer=TRUE)

segments(x0=as.Date(&quot;2012-09-09&quot;), y0=700, x1=as.Date(&quot;2012-09-09&quot;), y1=0, lty=5, col=&quot;grey80&quot;)
segments(x0=as.Date(&quot;2013-05-07&quot;), y0=700, x1=as.Date(&quot;2013-05-07&quot;), y1=0, lty=5, col=&quot;grey80&quot;)

text(x=as.Date(&quot;2012-07-01&quot;), y=840, &quot;Regime 1: 5-Arm Design \nApril 13, 2012 to September 9, 2012 \nEqual Assignment Probabilities&quot;, cex=.9, pos=1)
text(x=as.Date(&quot;2013-01-15&quot;), y=840, &quot;Regime 2: 3-Arm Design \nSeptember 10, 2012 to May 7, 2013 \nPr(Control)=.5, Pr(Monitoring)=Pr(Punitive)=.25&quot;, cex=.9, pos=1)
text(x=as.Date(&quot;2013-09-01&quot;), y=840, &quot;Regime 3: 3-Arm Design \nMay 8, 2013 to December 20, 2013 \nEqual Assignment Probabilities&quot;, cex=.9, pos=1)

dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<div class="figure">
<img src="data:image/png;base64,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" alt="Figure A2" />
<p class="caption"><strong>Figure A2</strong></p>
</div>
</div>
<div id="figure-a3-p.a-6-discrimination-levels-and-treatment-effects-using-the-subjective-discrimination-index" class="section level2">
<h2><span class="header-section-number">2.4</span> Figure A3 (p. A-6): Discrimination Levels and Treatment Effects using the Subjective Discrimination Index</h2>
<p><strong>Note:</strong> Analysis and figure construction code for Figure A3 is shown above in <em>“1.1 Figure 1: Main Results on Discrimination Levels and Treatment Effects”</em></p>
<div class="figure">
<img 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" alt="Figure A3: Net Discrimination in Subjective Discrimination Index" />
<p class="caption"><strong>Figure A3: Net Discrimination in Subjective Discrimination Index</strong></p>
</div>
</div>
<div id="figure-a4-p.a-17-estimates-of-tester-random-effects-on-outcome-measures" class="section level2">
<h2><span class="header-section-number">2.5</span> Figure A4 (p. A-17): Estimates of Tester Random Effects on Outcome Measures</h2>
<pre class="r"><code>ctx$weekday &lt;- as.factor(weekdays(as.Date(ctx$sfa_B4_apptdate, format=&quot;%m/%d/%y&quot;)))

anova_fig &lt;- data.frame(outcome = c(&quot;cb&quot;,&quot;off&quot;,&quot;qualpraise&quot;,&quot;sales&quot;,&quot;posedit&quot;,&quot;posbg&quot;,&quot;prof&quot;),
                        ylab = c(&quot;Callbacks&quot;, &quot;Offers&quot;, &quot;Praise&quot;, &quot;Perceived Sales Efforts&quot;, &quot;Positive Editorializing&quot;, &quot;Positive Reactions&quot;, &quot;Professionalism&quot;),
                        title = c(&quot;Tester Random Effects on Callback Incidence&quot;, &quot;Tester Random Effects on Offer Incidence&quot;,
                                  &quot;Tester Random Effects on Praise re: Qualifications to Rent&quot;, &quot;Tester Random Effects on Sales Efforts&quot;,
                                  &quot;Tester Random Effects on Positive Editorializing&quot;, &quot;Tester Random Effects on Positive Reactions to Background&quot;,
                                  &quot;Tester Random Effects on Professionalism&quot;))

# MAKE GRAPHS

for(ii in 1:nrow(anova_fig)){
  model &lt;- lmer(paste0(anova_fig$outcome[ii], &quot; ~ (1|tid) + (1|ttype) + callorder + weekday + partnered + numbr + anyskep + anyneg + (1|team_gender)&quot;), data=ctx)
  model_off &lt;- lmer(paste0(anova_fig$outcome[2], &quot; ~ (1|tid) + (1|ttype) + callorder + weekday + partnered + numbr + anyskep + anyneg + (1|team_gender)&quot;), data=ctx)
  ifelse(ii&gt;=2, se &lt;- se.ranef(model_off)[[1]][i,1], se &lt;- se.ranef(model)[[1]][i,1])
  i &lt;- 1:(length(ranef(model)[[1]][,1]))
  y &lt;- ranef(model)[[1]][,1] # individual tester RE
  types &lt;- c(rep(0,14), rep(1,19), rep(2,15))
  col &lt;- rainbow(3)[types+1]

  # output PDF file for tex
  pdf(paste0(&quot;out_figurea4&quot;,letters[ii],&quot;_anova_&quot;,anova_fig$outcome[ii],&quot;.pdf&quot;), width=8)
  plot(i, y, col = col, ylim=c(-1.0,1),xlim=c(1,50),xlab=&quot;Testers&quot;, ylab=anova_fig$ylab[ii],main=anova_fig$title[ii],pch=20,bty=&quot;n&quot;)
  legend(&quot;bottom&quot;, c(&quot;Black&quot;,&quot;Hispanic&quot;,&quot;White&quot;), pch=20, col=rainbow(3),horiz=T,bty=&quot;n&quot;)
  segments(i, y + 2*se, i, y - 2*se,  col = col)
  abline(a=0,b=0,lty=2)
  dev.off()

  # output PNG file for Rmd output
  png(paste0(&quot;out_figurea4&quot;,letters[ii],&quot;_anova_&quot;,anova_fig$outcome[ii],&quot;.png&quot;), width=800)
  par(0,0,0,0)
  plot(i, y, col = col, ylim=c(-1.0,1),xlim=c(1,50),xlab=&quot;Testers&quot;, ylab=anova_fig$ylab[ii],main=anova_fig$title[ii],pch=20,bty=&quot;n&quot;)
  legend(&quot;bottom&quot;, c(&quot;Black&quot;,&quot;Hispanic&quot;,&quot;White&quot;), pch=20, col=rainbow(3),horiz=T,bty=&quot;n&quot;)
  segments(i, y + 2*se, i, y - 2*se,  col = col)
  abline(a=0,b=0,lty=2)
  dev.off()
}</code></pre>
<div class="figure">
<img 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" alt="Figure A4, panel a: Post-Visit Callback" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel a: Post-Visit Callback</strong></p>
</div>
<div class="figure">
<img 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" alt="Figure A4, panel b: Post-Visit Offer" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel b: Post-Visit Offer</strong></p>
</div>
<div class="figure">
<img 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w9grLOdWj5zTtb20csIngffOboj7zRwcNaV6IUXXnAakDsdcxNxSvadY58v6yZqdbG623eqPQ+6RUZ3wdKAzzvY+/aZs2TfR8F3qH0fWX4NwvwyrrrqKj/PkUce6V8H36P211Iyf9serSxrtyDpWEE/Lfh9SOUzqQdUIm1vy7L2se95WzZdsAJZ/oZdwLpekBBAYD0Egp2k4AcmWJQOuvQ/braDZD/kOjjS6VkS/2NrP4xvvfWW/0HRo2K+/7Pt1F500UWRMQG2Q7z11lv7efTIqtMjjk67hETyWMCgR8j8az3S6osNAhDb4dUzAs7qFisFOwP2w2Y787YDHfyQ246n1deSnvlwNjbCftQtBWNf9EyOfx38mNrOjA789OsR7BwEYxss4LIfTgsM9Ciqsx062+mwaUEAokf4/GvrZ/7dd98525mzHUMLQvRMji8rCED07IXfybcdKluGrYOVbaa2LjYt3piZYMfQ5qn4eO211/xyLXCy92wnTo/OOj274YKyzM2m2c6wHjH28+nRVWdtcdppp/nXgVWwE3P11Vf7nR49s+Lrajs51pZ6FNjPr92anLV1su3BI0T9Z2UGO89mrd1ZnHbZ8cu0utnOUCrtE7VI/zQIQLTrmdOzKT4o1YH4frm2c2vbtaVkbZbK+kTvYMfa3oPPVjAGRI/A+3pceeWVvg5vv/22f23WlpJ95vxMFf4LDG0n2LYP256s/e0zZJ/HICVr7//+979Oj8T77dfsLTC25egZM7+IdLcHyxTtk0p7Wx5rIyvXAkXbTvXsqX9twbElGxth79tnzNKYMWPcpZde6j/rfkLUf7azr2d4/fz2+bXPlU1L1vZRi4g8Db5zrOyKD+36GZkv2XdO8B1nQbt9T+kZBd9uepbKf86CAMTqqN3GfFna5TKyfO326KdZfj2j6rSbpQ9irU4VAxA9M+L0bLCbMGGC/4wH20q8z1s6AUii78yg3radW7sPGTIkYkYAEmlKnoRcgAAk5A1I9f98gWAnqWIAYjuc9qNmgwftiLs9unbt6qdpFxGnXaj8To7NYzt7J554ov+hsR+cIOkVYvz8wQ617UTY/HaU15ZnOze2w2Q7T5aCH2ftjhIsIubfYGfAjrhrlzB/JNGWG/1DHWRcs2aN3zm+9tprnf1o23zBzkywg2vrGKTrr7/ez3PjjTf6ScHOSnAE0iZqdxc/jwUgtpMUnCmIPgob7NAHywkCkDfeeMMvN3C3MydBCo5kBkFLMD34GwQg//rXv5ydUYp+BEeq7QisraOd+QmSdtfw07Trj59kQYjNYzuItnNgbaHd7/w0G1BrKShrzpw5/rXtnGh3Cv/c/rOdHVuG7RBZSmV78DOu/c921Cy/tWGQdLxGpFztQhQJQBK1T5A3+BsEILbs6IedfdDudn62VNoslfWJ3sG2BVfc3oM2DgIQC1LsqLV2ifE7whYgWR2DeiX7zAXrGP032Km0MzzBmTqrlx2lD1Iq7W3zWv1swLYFl1ZHq5vVyVK624PlifZJpb0tTxCAWEBqyT5TVg87c2jJApOgXe3Ivp2RsCAkXjIHm992ii2l0vaxlhV852i3SWffEfZ9YmXbGTs7kxGcVbW8ib5zgu8O++wGyeYPUhCA2DKt3ta+2gU0eDsSPAVnhOyN3r17+3krBiDa5S+SLxX/dAKQeJ9J8w2C4OBAkAV9djbN1ocAJNIkPAm5QPVd11M/GSQEEPhDwMY9WLIBrdZ32h7adcFf5tYG9uqOo1hfdetzrGc//PgJ64euP0x/LKTCs2CZNibBlmf9vW18R+vWrcX6NQfJ+iynkmzcwfjx4/0lZ/UHW3TnxI97CPJan3Ybs6BHb33f9qCPvY1RiU4aQERe6g6if64/pP6vrZslGyMTJLtEZpCKiop8X3Qr38a0BMn60Fuyvv/RKShLd878ZBuXEiQ9quif6vdyMCnmXz2yLbqzsc7DLgmcagrawdZRd4R8W+gRf9+mugMoukPk20PPKkmTJk38Ys1Mj6rHLSLd7UF3dv2yAid7oTsuvr3suY1LCVJgZq8rtk8wT8W/NobHLmmqO99ibaQBmh8obPOl0mbprk/F8mO9tvWwwco//fSTaHDi++3bdnXMMcf42YN2ifeZi7XMYJoG72LjqnRn1A+816DHr7u9Hyw3XnvbPHoGxG/jNlbAtlm7jK8la/eqbA8+c9R/6bS3ZbMB9ZYqtrcNbNeul36Mio1psfFlNmbMtuNUUiptn2g5dtELvZqav8iFnkmQ559/3o8bswsyWEr2naMHCPx80Rf6CNbRv7H2Pw0GfHvY94+1TZBsuqXoS9kGVsE8wd/o79F0/IMybDnmFSvF+0zatmIP+z4M5rHn9j1MQiCXBNbdi8ilNWNdEPiTBeyH1pKeNfA7NrZzo/2U/cBC22GynTsLROxqWvajaoOobcd88uTJ/mF5gx19PQJmL/0Vq+yvXZ3Hlmf3IrEgxAbi2r0LgmQ7oukkq6ueFfA7AjZo3epmSc8++B3Z5557zgcgNrg8Vkq0Yx0MgLcBo0HSI67BU7/TbIGN/WjbTnyQbJ0sdejQIZjk/wYmwcTo18kCjyDP+v61ddKj9XYGWT744APfFhYU2sBW25Ezf+0+59/X7hu+ONuBtwH81naWgnoHbZvK9uAzrv1Pz7j5Z2Zp25ElPdsiNnjadli0n7ufZv8lap/ITBWe2E6drYMFeBZURSdbv2Rtlu762PIrmkSXGTy34NFSnz59xAbGW9AerF+yz1ywjHh/LWC0HWHb5mwgtC3bUrL2tgBFj+r7+1rYeuvYBh+MBuVUZXsI8gZ/02lvWw/bBmIlu0eGXeHM/mp3R9Ezk342+26KlYLlBDvVqbR9rOXEm2bBpCUb1G4p2XdOEHDb5y5IFizagHELloNkF/v44osv/AUe9GyHzJ07179lF66wZPf5sWTrZcFsrGTrGqRU/G37tQsm2MBxPZPts5pxrBRssxXfswMrNmDd6hXca8rWy4JqEgK5JEAAkkutybpklYBdFcmuJKT91P3Okv3Q2w6SHcG1HSf7YbLn9uNpV4+yS1LadNvxC84W2FVwLOmgcH+1LbsEqiW7HKrtKNkOkl1pJ/oIn71vOyDpJlumHfW2y83a1WIsBUfdLDiyK8lo1y8/PTir4V8k+c/qaD/M/fv3FwtgbEfBzrpEJwt6LNkZGTsCrV3V/FW8tIuIzxM9b3U8125hvi2sPYKHdvlKa9EFBQV+R8PWx67iY1fjsqsJ2RFcS7YeluyqXHZFHJvPbqoXBGRB21rb29msVLYHv8C1/1kAZFf/sZ1fazdrG7uqmSXbOY8+wrs2S7X+SdZm6a6PVS4wCbb3WBW2o/W2XdjybTvXMQ2R2ZJ95iIzJnhinz/txuODGu3u5696ZrMnam/bUbUrdtnnwq5cZEf2tVueLyX4rKS7PfjMUf9VV3vrOC5/pTS7IpNtq0EAEH1WMqrYSJtYYKvjmfz8ydo+On/F53YlM/vMWSCg3br8Z97msc+kpWTfOdbeFhDbelge+07RrqD+c2Dft0GyM1B2BsECQwvQ7YynJTuba21sV7izoMKuUmaXUo+Vor9HU/W3M3+WrJ52cMk+++km7a7ms9hVE+2MVRA0pbsc5kcgqwVC3oWM6iPwpwsE/dT1x6xSXewKVMEAXj2S6PtRB1cospmt37MNdNQddN+/V398ne6QRpajP2CRweE2mNmSXe1Fu/X4+a1/s125yq7sYikYA2J90BOloD929FWwbH4bzB30IdezHs7GEehOra+D7mT5MSL6o+4HreuOVWSMgdUhSHbzNv3S8zetC6bZOImgD7wN5rXxIDZPMAjd5tOdeKddsPx087CbAepRy2ARkatg2VVlLAUDkDUoi8wTDN7Us0iRadFPgjpY2RUfNuDfUipjQGw+G0hu41R0Z8Yvy+oejOew97XrhR/nYW5Wlu7g+RsP6pFNe9vpZTb92B97z/JaSrY9+Jmi/rPxQhrgRPqH28UE9L4Zvg+9zRaM0UnWPlGLjHkVrOj3o58na7Nk6xM9xsGWW3F7Dz5bwRiQoOxg+9Ed2WBS5G+yz1xkxrVPgjEg0VfBsrc0IPftZuOzbGxHsva2sQfaFdLnsQtPBBeMCOpele2hok+y9rZ62+dXd5ztqU/2GbZtzL6HLNky9KBA5AIX9p4Gzs6u/BUr2ZgEG69g89l3WDAGIVnbV1xW8J1jy7GHfcbts6E79s7GZAXjo5J959hy7QIbwVXK7DNtxsFV94IxIDbY3pK52xXBrMyxenUpS3pZdH/VPfsetc+wjTOz94MxcMEFA+zKZ9EpFX89M+PHtdjybByZfY/a8+D3IZXPpH2f68EY347mZHW0hy0n8I+uF88RCKOA/5bSjZqEAAIbUMDOKliKd1TaxoZYv187ghd91M3y2HvWTSfoD2zTLNl1+W3+eKfyy+eqnv+t/7PV3Y4cVjXZOthyrHtBomRnguxIeHSXskTz/9nvWXcLu79DvPWy9bZ1su5MFZP1Dw/e0x2NyNuJtofITFFPbGyCbQ9Wh6DLTNTbG/xpsjZLZ33ibe/prkSyz1y6ywvmT9TeuhPgzxDE2xZsGVXZHoKyg7/V1d7WXcw+18GZp2D5sf5alzQ7y2NdjKJTsraPnjed56l859h9gWz8RjrfS3YW0u6xYWdIgjOGdmNSu/GqdaEMzv4mqmsyf9sOrG7B2ZxEy0r0np09s7Li/W4kyst7CGS7AAFItrcQ9UMAAQQQQACBahGwGwxaFz5LNp7IxlpYtznr/qpXDItcPKBaCmMhCCAQV4AAJC4NbyCAAAIIIIBArgnofUdE70/kxxHZGUMLSOwsiF4KPddWlfVBIGsFCECytmmoGAIIIIAAAghsKAHrKlWxy+uGKovlIoDAugJ/dDpedzqvEEAAAQQQQACBnBUg+MjZpmXFQiBAABKCRqKKCCCAAAIIIIAAAgjkigABSK60JOuBAAIIIIAAAggggEAIBAhAQtBIVBEBBBBAAAEEEEAAgVwRIADJlZZkPRBAAAEEEEAAAQQQCIEAAUgIGokqIoAAAggggAACCCCQKwIEILnSkqwHAggggAACCCCAAAIhECAACUEjUUUEEEAAAQQQQAABBHJFgAAkV1qS9UAAAQQQQAABBBBAIAQCBCAhaCSqiAACCCCAAAIIIIBArggQgORKS7IeCCCAAAIIIIAAAgiEQIAAJASNRBURQAABBBBAAAEEEMgVAQKQXGlJ1gMBBBBAAAEEEEAAgRAIEICEoJGoIgIIIIAAAggggAACuSJAAJIrLcl6IIAAAggggAACCCAQAgECkBA0ElVEAAEEEEAAAQQQQCBXBAhAcqUlWQ8EEEAAAQQQQAABBEIgQAASgkaiiggggAACCCCAAAII5IoAAUiutCTrgQACCCCAAAIIIIBACAQIQELQSFQRAQQQQAABBBBAAIFcESAAyZWWZD0QQAABBBBAAAEEEAiBAAFICBqJKiKAAAIIIIAAAgggkCsCBCC50pKsBwIIIIAAAggggAACIRAgAAlBI1FFBBBAAAEEEEAAAQRyRYAAJFdakvVAAAEEEEAAAQQQQCAEAlkXgJSUlMjSpUtDQEcVEUAAAQQQQAABBBBAIF2BrAhAioqK5JprrpFWrVpJ7dq1pWnTplK/fn1p3769DBkyJN11Yn4EEEAAAQQQQAABBBDIUoH8bKjXJZdcIgsWLJCRI0dKmzZtfPCxYsUKmT59uvTp00dWr14tvXr1yoaqUgcEEEAAAQQQQAABBBBYD4E8p2k98ldL1u22204mTpwoLVu2rLS8SZMmSd++fWX06NGV3mMCAggggAACCCCAAAIIhEsgK7pgWVerMWPGxJQbMWKENG/ePOZ7TEQAAQQQQAABBBBAAIFwCWTFGZApU6ZIz549pWHDhrL99ttLo0aNZPny5fLVV1+JDUofNWqUbLPNNuGSpbYIIIAAAggggAACCCBQSSArAhCrlY3zsG5Yc+fO9eNB7KzHjjvuKN26dZO8vLxKFWcCAggggAACCCCAAAIIhE8gawKQeHQzZ86UVatWSefOnePNEpn+/vvvi40ZiZUWLVoku+++u5x22mmx3mYaAggggAACCCCAAAIIZEAgK66ClWg9hw0bJt999508/PDDiWbz79kg9g4dOsScb/z48fLNN9/EfI+JCCCAAAIIIIAAAgggkBmBrD8DUl0MFsjYWZDevXtX1yJZDgIIIIAAAggggAACCKQpkBVXwUqzzsyOAAIIIIAAAggggAACIRUgAAlpw1FtBBBAAAEEEEAAAQTCKJAVY0DuuusuKS4ujuu38847y3HHHRf3fd5AAAEEEEAAAQQQQACBcAhkRQBil969//775ayzzpL69etXkuNGhJVImIAAAggggAACCCCAQCgFsiIAue+++6SsrMw/Bg4cGEpIKo0AAggggAACCCCAAALJBbJmDMhtt90mK1askF9//TV5rZkDAQQQQAABBBBAAAEEQimQFWdATK5Bgwby9NNPhxKRSiOAAAIIIIAAAggggEBqAllzBiS16jIXAggggAACCCCAAAIIhFmAACTMrUfdEUAAAQQQQAABBBAImQABSMgajOoigAACCCCAAAIIIBBmAQKQMLcedUcAAQQQQAABBBBAIGQCBCAhazCqiwACCCCAAAIIIIBAmAUIQMLcetQdAQQQQAABBBBAAIGQCRCAhKzBqC4CCCCAAAIIIIAAAmEWIAAJc+tRdwQQQAABBBBAAAEEQiZAABKyBqO6CCCAAAIIIIAAAgiEWYAAJMytR90RQAABBBBAAAEEEAiZAAFIyBqM6iKAAAIIIIAAAgggEGYBApAwtx51RwABBBBAAAEEEEAgZAIEICFrMKqLAAIIIIAAAggggECYBQhAwtx61B0BBBBAAAEEEEAAgZAJEICErMGoLgIIIIAAAggggAACYRYgAAlz61F3BBBAAAEEEEAAAQRCJkAAErIGo7oIIIAAAggggAACCIRZgAAkzK1H3RFAAAEEEEAAAQQQCJkAAUjIGozqIoAAAggggAACCCAQZgECkDC3HnVHAAEEEEAAAQQQQCBkAgQgIWswqosAAggggAACCCCAQJgFCEDC3HrUHQEEEEAAAQQQQACBkAkQgISswaguAggggAACCCCAAAJhFiAACXPrUXcEEEAAAQQQQAABBEImQAASsgajuggggAACCCCAAAIIhFmAACTMrUfdEUAAAQQQQAABBBAImQABSMgajOoigAACCCCAAAIIIBBmAQKQMLcedUcAAQQQQAABBBBAIGQCBCAhazCqiwACCCCAAAIIIIBAmAUIQMLcetQdAQQQQAABBBBAAIGQCRCAhKzBqC4CCCCAAAIIIIAAAmEWIAAJc+tRdwQQQAABBBBAAAEEQiZAABKyBqO6CCCAAAIIIIAAAgiEWYAAJMytR90RQAABBBBAAAEEEAiZAAFIyBqM6iKAAAIIIIAAAgggEGYBApAwtx51RwABBBBAAAEEEEAgZAIEICFrMKqLAAIIIIAAAggggECYBQhAwtx61B0BBBBAAAEEEEAAgZAJEICErMGoLgIIIIAAAggggAACYRYgAAlz61F3BBBAAAEEEEAAAQRCJkAAErIGo7oIIIAAAggggAACCIRZgAAkzK1H3RFAAAEEEEAAAQQQCJkAAUjIGozqIoAAAggggAACCCAQZgECkDC3HnVHAAEEEEAAAQQQQCBkAgQgIWswqosAAggggAACCCCAQJgFCEDC3HrUHQEEEEAAAQQQQACBkAkQgISswaguAggggAACCCCAAAJhFiAACXPrUXcEEEAAAQQQQAABBEImQAASsgajuggggAACCCCAAAIIhFmAACTMrUfdEUAAAQQQQAABBBAImQABSMgajOoigAACCCCAAAIIIBBmAQKQMLcedUcAAQQQQAABBBBAIGQCBCAhazCqiwACCCCAAAIIIIBAmAUIQMLcetQdAQQQQAABBBBAAIGQCRCAhKzBqC4CCCCAAAIIIIAAAmEWIAAJc+tRdwQQQAABBBBAAAEEQiZAABKyBqO6CCCAAAIIIIAAAgiEWYAAJMytR90RQAABBBBAAAEEEAiZAAFIyBqM6iKAAAIIIIAAAgggEGYBApAwtx51RwABBBBAAAEEEEAgZAIEICFrMKqLAAIIIIAAAggggECYBQhAwtx61B0BBBBAAAEEEEAAgZAJEICErMGoLgIIIIAAAggggAACYRbI2gBk0aJFUlJSEmZb6o4AAggggAACCCCAAAIVBLIiADnzzDNlxowZvmozZ86UgoICadWqlbRs2VL+/ve/S3FxcYVq8xIBBBBAAAEEEEAAAQTCKJAVAci0adPkt99+83633nqr7LzzzjJ//nyZMGGCzJ07V2waCQEEEEAAAQQQQAABBMIvkBUBSDTj6NGjpV+/ftK0aVNp27at3HTTTTJ27NjoWXiOAAIIIIAAAggggAACIRXImgDEznb89NNPsu+++8rixYsjnFOnTpXOnTtHXvMEAQQQQAABBBBAAAEEwiuQnw1VP/300+X111+XG2+8UZYvXy5169aVZ5991p8JGThwoLz77rvZUE3qgAACCCCAAAIIIIAAAuspkOc0recyqjX7vHnzZMWKFdKuXTuZNGmStG/fXho0aLDeZQwbNkzsylq9e/de72WxAAQQQAABBBBAAAEEEKiaQFacAYmu+lZbbSX2sGTdsdJJZWVlYo9YqbS0VLIs1opVTaYhgAACCCCAAAIIIJDTAlkXgFTUtsvyrlq1KqVxIEOGDJHnnnuu4iL8659//ln222+/mO8xEQEEEEAAAQQQQAABBDIjkHVdsCqutl0F67vvvpOHH3644ltpvaYLVlpczIwAAggggAACCCCAwAYRyLozIHb385UrV0qTJk38Cl933XUbZMVZKAIIIIAAAggggAACCGReICsuw1tUVCTXXHONv/t57dq1/T1A6tev7wegW7cqEgIIIIAAAggggAACCOSGQFacAbnkkktkwYIFMnLkSGnTpo1Y8GFXwpo+fbr06dNHVq9eLb169coNcdYCAQQQQAABBBBAAIGNWCArzoC89dZbMnjwYOnYsaO/5G5eXp40btxYunTpIv3795fhw4dvxE3EqiOAAAIIIIAAAgggkDsCWRGA2L0+xowZE1N1xIgR0rx585jvMREBBBBAAAEEEEAAAQTCJZAVXbAKCwulZ8+ecs8998j2228vjRo18ndE/+qrr8QGpY8aNSpcqtQWAQQQQAABBBBAAAEEYgpkRQDSuXNnmTJlikycOFHmzp3rx4PYWQ8b99GtWzexLlkkBBBAAAEEEEAAAQQQCL9AVgQgxli3bl3p3r17+EVZAwQQQAABBBBAAAEEEIgrkBVjQOLWjjcQQAABBBBAAAEEEEAgpwQIQHKqOVkZBBBAAAEEEEAAAQSyW4AAJLvbh9ohgAACCCCAAAIIIJBTAgQgOdWcrAwCCCCAAAIIIIAAAtktQACS3e1D7RBAAAEEEEAAAQQQyCkBApCcak5WBgEEEEAAAQQQQACB7BYgAMnu9qF2CCCAAAIIIIAAAgjklAABSE41JyuDAAIIIIAAAggggEB2CxCAZHf7UDsEEEAAAQQQQAABBHJKgAAkp5qTlUEAAQQQQAABBBBAILsFCECyu32oHQIIIIAAAggggAACOSVAAJJTzcnKIIAAAggggAACCCCQ3QIEINndPtQOAQQQQAABBBBAAIGcEiAAyanmZGUQQAABBBBAAAEEEMhuAQKQ7G4faocAAggggAACCCCAQE4JEIDkVHOyMggggAACCCCAAAIIZLcAAUh2tw+1QwABBBBAAAEEEEAgpwQIQHKqOVkZBBBAAAEEEEAAAQSyW4AAJLvbh9ohgAACCCCAAAIIIJBTAgQgOdWcrAwCCCCAAAIIIIAAAtktQACS3e1D7RBAAAEEEEAAAQQQyCkBApCcak5WBgEEEEAAAQQQQACB7BZIGIDMmzdPiouLZeXKldK/f3955ZVXsnttqB0CCCCAAAIIIIAAAghktUB+vNpNmDBBDjvsMJk5c6YUFhbK5MmTpaioSJYsWSLnnXdevGxMRwABBBBAAAEEEEAAAQTiCsQ9A/LUU0/JI488Ii1atJAXXnhBnnzySbFpL774YtyF8QYCCCCAAAIIIIAAAgggkEggbgCyfPlyad68uYwfP14233xzad++vaxZs0YaNWqUaHm8hwACCCCAAAIIIIAAAgjEFYjbBaugoED69OkjpaWlcvbZZ8v06dPlrLPOkmuvvTbuwngDAQQQQAABBBBAAAEEEEgkEDcA6dmzp2y22WaybNkyOemkk+Tbb7+VQYMGSffu3RMtj/cQQAABBBBAAAEEEEAAgbgCcQMQy3H44YdHMu6www5iDxICCCCAAAIIIIAAAgggUFWBuAGInfno3bu3TJ061V/9KiigR48ecu+99wYv+YsAAggggAACCCCAAAIIpCwQNwC5/fbbxQaiDxgwQBo0aBBZYNOmTSPPeYIAAggggAACCCCAAAIIpCMQNwCxmxDaGRDGfKTDybwIIIAAAggggAACCCCQSCDuZXhPOOEEGTp0qCxcuDBRft5DAAEEEEAAAQQQQAABBFIWiBuAzJ8/X0aNGiVbbLGF7LjjjrLzzjv7h12al4QAAggggAACCCCAAAIIVEUgbheso48+Wvbcc09/GV47C9K6dWupW7euMAakKszkQQABBBBAAAEEEEAAAROIG4BsueWWcv3118vjjz/ugw67KtZxxx0nzzzzDHIIIIAAAggggAACCCCAQJUE4nbBGjx4sMyePdvfAX3x4sUya9Yscc7JbbfdVqWCyIQAAggggAACCCCAAAIIxA1AJk2aJFdddZW0a9fOK7Vp08afERk3bhxqCCCAAAIIIIAAAggggECVBOIGIF27dpXx48evs1B73bx583Wm8QIBBBBAAAEEEEAAAQQQSFUg7hiQk08+WTp06CB2xmP//feXTz75RD777DP/OtWFMx8CCCCAAAIIIIAAAgggEC0Q9wxIs2bNZOrUqXLGGWdIWVmZFBQUyLRp02S33XaLzs9zBBBAAAEEEEAAAQQQQCBlgbhnQGwJFoRceumlKS+MGRFAAAEEEEAAAQQQQACBRAKVApB99tlHbr/9dpkwYYIMGTKkUt4ePXpI//79K01nAgIIIIAAAggggAACCCCQTKBSAPLQQw/JtttuKy1atJDu3btLrVq1IstYs2aNFBcXR17zBAEEEEAAAQQQQAABBBBIR6DSGBC77G6dOnVk0KBBsnTpUtl1110jj++//96fHUmnAOZFAAEEEEAAAQQQQAABBAKBSmdAHnvsMenVq5d/f8CAAcF8/m/Dhg3lv//97zrTeIEAAggggAACCCCAAAIIpCpQ6QzIxRdf7LtZ3XXXXTJx4kT/3LpdlZSUyIoVK6R3796pLpv5ckjATf5Eyl56JYfWiFWpLoHb5Jq0F/WsPCzfy7dp5yPD+gk8J4/KXJm1fgshNwIIIIAAAuspUCkAseXl5+fLZZddJnPmzPGD0e314MGD/aD00tLS9SyS7GEUcAsXiXw7J4xVD3Wdy8aOk7L7H8jqdfhcPk67ft/ITPlVVqadL1MZVslvskR+yVRxGStnjnyd1e4Zg6Cg0Ano7ggJgWoR+P13kUW6S0P6cwViBiBWpZdfflnuueceadmypa9ht27d5Nlnn5Unnnjiz60xpSOwMQnohR/k198yssbuf5PFfTUjI2VleyGfykR5Th7J9mpSPwQ2GoFzztloVpUV3cACU6aI3HvvBi6ExScViBuAvPHGG3LzzTdL27Zt/ULat2/vA5IXX3wx6UKZAYFAoPTk04Kn/M1yAffx/8R9OT3La0n1EEAAAQQQQCDsAnEDkG222UZGjx69zvqNGzdOGjVqtM40XiCQSMAtyr1uLInWl/cQyDWB32WVTJGPcm21WB8EEEhBYPz4FGbaSGaZO3cjWdEMrWalq2AF5Z577rly6KGHysiRI2XfffeVL774Qn7++WexMyMkBBBAAIGNQ2CpjoV5RgZLZ9ln41hh1hIBBCIC//mPyNixkZcb9ZOzz8aiOjeAuAHIVlttJZMmTZJ33nlHZs2aJeeff7506dJFatSIe9KkOuvFshBAAAEEEEAgiwUuv1zk7ruzuIJUDQEEslYgYTTRuHFjOfHEE+Vf//qX7Lfffj74+N0uH0DKCgGnN4Ysu5n7smRFY1AJBBBYb4Fp2tnrZRma9nKel8fSzlPVDNfIxVXNmlY+u0rcWzI8rTw2893SN2NXOvv007SrJ86Jf6SfMzM5Vq0SefPNzJRFKX8ImPsrXOn/D5CN4FncAOSXX36Rk046STp16iQ2AH2XXXaRNm3ayHnnnbcRsIRkFVevEff9DyGpLNVEAIFYAt/JN/KT/BjrrYTTcnFcxkpZJvP1DjHpple1k1im0lyZnZGiVspyGV2FAGS2zJASKc5IHatSiN7rWJ5+uio5M5Nn+XKRJ59Mv6wZegFBvV0aqYoCv/4q8txzVcxMtlAKxA1A7BK8qzQkveCCC2TrrbeWwsJCPwD9mmvSv+lYKGWoNALVLFD29jviuI9ONasmX9xIeVF3x7J3h+wDeUc+lUnJV6TCHHfItRWmZNdLO5OxRlZnV6WoDQIbSMDGSixbtoEWzmIRyEGBuAHIN998I1dccYWcraNu5s2b58+GDBkyROwO6SQEEEhfwN0zQKSoKP2M5FgvgeHytBTprjApswJvyau4ryV/QP6r5yWmZrYBKC1nBY44ImdXjRXbiATiBiA2CP17HWPQoEED3WcqksWLF0vTpk39tI3Ih1UNkUDZfQNDVFuqigACG4vACu1ORRCc+da2G85pb/KcS3Z/2nTTSy+J2IOEQLYIxA1AbKxHnz59ZLxeBPrYY4+Vo48+Wk444QQ57LDDsqXu1CNHBcoGPyxlI0alvXbu5VfTzlP27nvicvEXKm0JMiCAAAK5JfDCCyKzMzNkJ+vhVq4UsXEWJASyRSDuZXht0PnMmTOlZs2a/vK7Dz74oGy66aZyyimnZEvds7IeTi/x4V4bITX+ckxa9Su7427JO/E4ydOB/ht9WqPdlIoz02ffvTpC8jbfXGSzzTZ6dgDWX+AleVJOlDPXf0EsYYML2CD+2lJHdpXdNnhZFIAAAgggsK5A3DMgNlvLli2lefPmegrzF7n44ovljDPOkNq1a6+7BF6tK1BWJu6+B9adlsIr9933Ir9nZsCmmzpNL4Oo10IkVUnALVokZU8/W6W8mcpU9pwe+iNlXMACEFI4BGbqmIxvZWY4KruBa7lCrz72o8zdwKWw+I1F4O23Rb77bmNZW9azqgJxA5Ay3ZG+6aabpGPHjr7b1bvvvivHHXecLNKdL1K4Bcqu+jeDodenCfU6jW7s++uzhA2et+zBhzd4GRSAAAK5IfCVfCEvVeH+K7mx9qxFdQuMGyfyY/pXFq9SNezSvXQtqxLdn54pbgDy0EMPyXvvvScvv/yyr+TBBx8sNjDdppMQQACBTAvY/SFukD6ZLpbyEEAAgQ0i8MUXIo8+ukEWvdEsdLjeq/O33zaa1c2pFY0bgNjg8yuvvFK23HJLv8K1atXyg9ItKNmQqUTv5LN06dINWQTLRgCBEAqUSqmsEn5pQth0VBkBBGII2E0P6aoUA4ZJG4VA3ACkVatW/gpY0QqvvvqqbLHFFtGTquW5XebXbnBoZdoYE7vcb/369f0d2O3eIyQEEEAAAQQQQAABBBDIDYG4V8G67LLLZK+99pK3dTTRTz/95K+ENXfuXHnnnXeqfc0vueQSWbBggYwcOVLa6FWgLPhYsWKFTJ8+3Z91Wb16tfTq1avay2WBCCCAAAIIIIAAAgggkFmBuAFIixYtfADw/PPP+5sPHnjggdKtWzf5/PPPq72Gb731lkycONFfdStYeOPGjX3Q079/f+nbty8BSADDXwQQQAABBBBAAAEEQixQKQBZtWqVXH/99TJt2jQ58cQT5fzzz5e8vDyZMWOGdO/e3Y8JsaCkOlP79u1lzJgxctppp1Va7IgRI/ylgCu9wQQEEEAAAQQQQAABBBAInUClMSDW9coGmtsdz++8807fBesFvZ3o7rvv7q+CNWDAgGpfycLCQrHH3nvv7YOQiy66SE499VTp1KmTvPLKK3LzzTdXe5kbaoH+Hhu/LBZntx1NMbmJk8SNfkdKL79anI6HSTWVjXtf3KzZ4jRoTDU5u4TsYq3f17NSzSKutFTchxPEfTpFnF4kINXkpnwmsuBncdO/SjVLeVk6v/tiasp5bEYrwy1eIk677qWarI3c7G+k7KOPU83i53P/+0Tc3O/EadfAVJPTkYZu3k/eMeU85j7+QzHHtNxn6L0NFi5Kr41//13c5E/Fb4tp3CPmYxkvi+RnfSxIdbX0jgNLZLZ8JZNkbMp5bMal8ousFB21mUayei2Un/Qio5NTzmWD3SfLhzJdPpdi/ZdqmiDvyQ96L4V3ZUSqWWSN/psqn2pJH4newjTlfNM0z2JVt0eqydxn6VpNEr1GZhrJ5v9e5ujw/9Rvo/yzzFf5efKJTEi5JGvbZ+VheVWe0/K+TTnfMHlcvpYv5W7pm5bhd1rGHM2ZTvpMPlbzhbrFz085m62Xbe+2TaWTPtH556lEOu4fyDu6PU2Wm+Uq3YpLUy7Otie7J0o6efSYpPz8s8jUNL6q7eftk09EPtav3DS+ZuSDD/SnRL9ivv8+5VXy8+q1dOSxx0T0rgIpJbv/rXa60PGv5XVMKZPOpD+pMl83iU8/TTVH+Xzml+6dDbQnvM+j94lOOdnP9pQp5Y+UM+mMH31U3sbpuM+ZI6J3bZCBA0XS2F2QCfpVYZfuTeeSutpxxm9PN96Yehvbdvf+++XbbRq7WzJ0qOiBeZGrr05v203He6ObV29It07q0KGDmzBhgp/2xhtvOL0PiNMbEjq9HO8681X3i99//91p4OMee+wxd8stt7iHH37YjR071un9SKqlKA2i3MCBA6tlWfEWUvr6SFfcqLkrzq/viqWOK/vxx3izRqaXzZ7t57X5i2tu4kqOODryXqInJf+4whU3aKp56pWXtXBhotn9e2VLl7rixpu74toNfZ7St95OmsdmKO5+uCveZNPy9WrRypWtWpU0X+mHE1xxnUauOK9ueVnvvJc0j7V18e77uuL6TXwdS07pmTSPzVD6xujy9aqVhvuaNVqO+pmF1rGkb2FKZZXceIu28WauuFYDn7ds8eKk+crmzSt3qLGJtyh5+NGkeWyG4kOOdMX11MK2p822cmW//ZY0X9mUz8rd124XpePeT56npMQVt96h3KK2rleXA1P63L3onnDt3aZuB1fHbe9quxluatKyfner3I5uE9d27eM697ekeWyGiW6s29tt5dq5hu4vbl9XpP+SpSXul7Xl1PP1e8U9nSyLf/8sd5TbVderravndnct3G/u16T53nUj3E46vznY4yX3ZNI8Ja7EHaS5bJ12cvXdSa6bK9V/ydLb7jXX0TX1jlbWNyqfLJm7rU/w+Je7KFkW//5t7hrXwZdV16/XAjcvab7FbpHOWydSv2fdI0nzmIWtU7At7eIaaUnfJc03xN3n7czBDB90tyfNYzOcp1tRJ9fM7ax5LnNnpZTnDfeyWjTROpZbpLK9r3Fr/GfEyrFt/j/u7ymVdZ+7WT2a+Ty2bgvdT0nzfeY+jmx/1s7XuF5J89gM/3IXrrVo4PZz27nV7vek+b74wrlatZyrUcN255wbOTJpFv1Oca5dO+fq1i3Pu+eeTo+rJM/3zDPO1avnXF5eeVmTJyfPs3x5+bxWt5o1nTv88OR5bI6uXcvrZvmsvEmTkudbscK5tm3/yPfEE8nz2BxDhjjXrJlzdeo4d8wxzvsky/n11+Xz2zpZHd95J1mO8vfN2gxr13auSxf9zUz+NeOGD3eufv0/2vjTT5OXtWTJuu4HHphaWXfd5VyDBs7l55fnnz8/eVnTpzvdNy2f3/L1Sm1zdyefXL5elsfWb9my5GU9ol9hQd1su+/XL3ke5kguUKkLlg0479y5sw/Ett9+ez8OZIqGztZNakOmunXr+i5eFcuYqWG+dQsL6lTx/ejXr732mj9jEz0teD579mw/sP2HH34IJvmzLnZ54SDZWBO7IleQ7KxMWu+f1lOKosL3G/oPlLq33xIszo9lqbj8muP0UIvdXV7LvaG0SIomfiA1/v1vnydR+WWDHpB+RaVSS/JE6tQW9+rr0u+H7xLW//oje0jRcj1Uszb1u7avbHLYocHLmPXLnzNX5HO9WLkeIb9BSqRoxVLJO+tsydu+TUK/sqHPSL81q8vrpyW4u+6Vvu+PTVi/vrrcNV/qGKM15W3Q78OJUnfal5LXfldfx3jtU3buhSJ6ZsfXz+Y88SSp0f2ghPVzuty+pSVSa217uyefkr5O1y9J+6++9XYRO0ymqV/tTSTvpeFS84Jz/eu49bvjHvutkBs0/PBrds21UuObWYnrt2Sp9P30M6kVObvlpO9550vxttv4suy/WNvH6seeUL/yS9X2k5pS+7Y7RbodkLB+bvwH/rDTDUWryus37XPJO+88uXHw4ITb//uFL8jvtf44+3ZR3/OlS1H3hPWbVjRFz2SU6ZZULE0La8oHtd6OzB/Pz2b4qxwhi/sW6xlCPfonU+T/5ER5vvClhPWrX1gipbX+OAp8dd8r5aOiPw7XxvJbVLRQ3pSxel5ita9fXq0aMlHGyCFydMzPR/D98KQMlIV9V/v6WX3/I9fLMYWnJqxfQeGhsqLWMjVfY1nko75fyEVF58tm0sK/jlU/2z6fkgf1XMSKtfXLkye07BtkQML6vaXnFZb2LZXiovJt92l5Qbef+xLWz8p/ttZDeunj8jMfS/uWyXlF50gH2T1h/T6S9/UsQXn75tWy+t0vp8p5Cetnd+G25a9cW786uoV8VvixbFmrtS/L/ou1fdj2Y9uSpYV9f5e7i/rrkpb41/H8FujZi5EyWhoWlorVz87w2I34nuv7UsLP/8W6fS8v+uMM3OPqd2utwb4s+y9W/d6q9ap+Q+dpDYtlSd8SebzoaT0fkif1pH7Mz2/w/fOY3CsN1tavpn6OR8qLMqfvooT1+2ffq2VxUXCGukTGF+pn64+ft5j1W1RrgYzSZdtZFqvf0qKf5Sw5Q7aTHRPWz3phFxcX6lqXF3DHHXa0PPHv57nn9pU5c4okOHE8f36hHr2upT0fyglj+dnnS3uE6z6AF9b/iuRc/bo96qjY33+Bn52dadCgUI+m1xI9kSxffy1y+eV9pU6d8t8WW1qs7eOLL4qCr3f9yi6UceNqyT772Nyx29fqd49+vX/zjWg5fXWuIrniCpGv9KR/rOUH9bP1GTDgD78P9eTYGWf0ldatE9fv6af126L860LLKtTeIbXkkEOsdvHrZ2cJrH6rVpXXz85YnXOOyCOPJN6/efHFQr2/xh8b0Dnn9JUePRLXz/zW7s6oR6HMnVtL5s0TvcJp/PpZ3W+4wX6Cyutnr08/XWT06MT1q1evUM+KldfPzrQ8+2xfadw4cf0WLy4S3U1ca1ioFzyqJToEWU4+OXH93njDzuaU189+/h94QPTKrYnrl6j9bR2z7X2rU6ZTpQBEYxapUaOGr0d+fr5+IFpv8OAj0UoPGzZMr5P9negZkUSz+ff20W8Ku4pWrDR69Gg/mP7YY4+NvG3rF50KCgr0Q6PfVmtTuu/32LmdlE7W88uWataU/LU7quUTRGIuv93OOm+5dw+pIaWrSyR/bR0TlV/ywsuS/+3c8kWXOZEmTaSgY/uE9T9q9z2lZLJ2i1q7jvnffBtUzf+NWb+GDTTAqePf9/X7fY3U1O55FhQkql/prG8l/xX9pFvdauSJW74i9vpH1aBAL3RQNEI/6WuW+an58+aLbFI3MkfM+tm7u7YT+WmB+Prptpu3dWupqYaJ6lfWeFPJf1+/mVev/Taf813y+un2sWboc6K/ouX106A5r0H95PVrs52Izttj9aryThFqaG2cqH5uyRLJf/3NyLJFu/UV6C9N2a67RKbFyl/05Qzf1csCHtu63aJfIvPH88vbdFNtoxrlfjb3yt+kpq5rrOVHfz5+yZ+hXVF+9MvP1x2RtgXbybGliT9fzUsbaReR93WnVj9nWkHrshSkePWz97eVHeT3gpm67Vq2mtJR2iWt39f5n0ptqRPZwZeCVUnrt6R0kXwgw3UXVn/ItH7L9VljaeKrmKh+u8m+MqZgjH60SnT3soZsLc2T1q9xfj1dk5rB6ktZwa9yVOlR0lK28tPi+U+XCeVdm7R++i0T6TaTqH6NZVP+baDyAAA2R0lEQVTZtGATWbX2s6+7+knrZ+VvqzuiU7U0S80KGsj+pV3lQDk8Yf3qaoA5R/R7Jl9/qTVZcGEpUf1ayJbSvKCRlJUG3byKZOf8Dj5f8F+s/PvLoaoxxrdxo4LasltpBzlW/1mK5/eNzNCOVG/K6vzy+01Z1758/Rdr+UHZ9nePgs7yUek4P6mGtZv6R6dY+RtKI79sm69eQQ1dv5VyjNavvv6LVz+bd4KGHD/q58tSXQ1XLGApKNg74ff70QVHy8zSjzXUsZ0w3Qrzy39X/EL0v1j1q6NLb6TbhgUgVr/8UifdpZt+vvZKWD/rkvP997rt6edRh4j6Lkixlh+UbX8PP7xAXn21NCoAyRf76glSvPza+1v0+KGmAv1ZLfU7s/YTmchv222tG9AfDTR3rshxxxVowJ349/3dd0vFggFLzZrlazfw8uf2f7z6tWypYZjuB5eWFuhcpXqgUyRZ/X7Rr+VXXsmX4HioHffq3LlA9tsvcf1mzy71Pz/lXcry9Qqlyeun1/Px9TM/q58dIz3iiMR+ttQ5c/LXutu8Ii1aFOh6Ja5f69alfoe+/Dhevr/HyWabldcxnp+9u4v+rE2aVF6/TTYxi+T1W7gwX2xePTbqU1lZ8votXVoqL71U3p1PS/AWuuvkU6L6Ha5fea+9VqDBaant2knbtsnrl2j7tAKz7f1yhQz/X/EkSTM9JzhPu4wsWrTITdZznbpD75/ba3vo5XErZgnF60x0wSqb+bXvYlNcV7seddrTlWm3slRS6fDXXHGTFq74mONdmZ3DTCGVfjG1vKwGzVzJeal1pyhbudIVN9tCu+hotyPrSvW5nkdPIZW++np5WU1aupI77k4hh55O1u2keLMty8vav7uzslNJJf+4vLx7U1Mta+CDqWRxEXfr8rVLp5S6iJXpOeiSv5xU3l2p5TaudMy4lMoq/eh/f7if+teU8pQVFbnifbuVd3HaZbeUuubZgktHvlFellncrueoU0hlej7Zdy2zNu64R8rbU8m9AyJd7EpffDmFkpx2tFngu3xYF6Lz3XHaiSaF/hS65Otcb+360kA7HO2gnbY+Sams591jkbKO0o5R1rUlWbJ5jnZ7ri1rRzfHzUqWxb8/1o32ZVn3q0Epdumxsk52B/ruVH3cmdqNZXVKZQ1xA3xXpb20e9lr7vmU8vzkfoxYWJmpWNiC+7l/+G5K6bibmXUBsq5HqXbpse5xJ7oDvPvBKvKddhJLJVn3rv1cG98tzbrcpZLKXJm7yV3pu2/dqR2c0t0GO7vNneVLJVk3KLOw7f10d1jK7te4i31Xry5uW+0k9UEqRbnZ2q0ucL/cnZ1SHptpqG6x1nXrMt0GU+k6aHlGaYdBK6uT2yzlNrav8803L+/S07mz02GFtqTk6b77yrtEbbaZc48/nnx+m0N3O3z3F+uqdNRRTodJppZv8GDnNtnEOavft9+mludX7W1p3amsftbtJpWkvXldp07lXaO23NK5zz9PJZdz115bbmFdo3baKbWuSta1rHHjcvcdd3RuwYLUyrIuTtZtq2lT5+6/P7U81qvbutiZe/fuToc8ppbv6afLuzZZt6+5c1PLM2OGHjGT8nzWRSrVdPvt5XmsC1sqXalsue9rr2Qra9NNnbvmmlRLcu7f/y7vJnbVVdo9ujj1fMwZX0CbYd1kAYjGQHEfJ6ezday76JReFWvLLklxJzylBa6dKRMBiBVVqt/ExV26OdvpTCeV/O0frmzal+lk8TveJRf2Ti+P9ffvepAr/eGHtPLZznBpijvB0QsuPvDQ6JcpPS+5/gZXmuI4iWCBfsd7r66uzH4N0kglZ5/vSsentlMQLFbPcrmSU88IXqb8t6THMSkFR9ELLH1ztCu9Vb9l00hl1sb7HJC2Ren9D7jSYS+lUVL5rKe5Q9LOc6O7wn3pPksr31vuVd2t6uFWuvQOgvzVHZFSH/royox3b7uB7tboSSk9r4rFUzpyYYQbltLyg5msn/6Jbv+Ud7iDfLe4q1MO+oI8NhbjSndO8DLlv+do+JfK2IXoBU5w7+mojpuiJ6X0vCruA9yN7hGX2gGVoBIW7J2kwVUqY3WCPPa3r7vUTUgxqAryfeNm6gip/wtepvy3lzvFLXUpRgRrl2qfrUJ3ecplBDMeeGDwLPW/tmM/dGjq8wdzVqWsf/1Lx49NDJaQ2l8bf3DaaanNGz1Xjx7O6ZDOtNIDDzh3+unOpfOzpV/vbp99nF57Jq2iXP/+zg0YkF4em7sq7hZcfZDez6oPEi2QSDf9n35EUg3EgmV/+GF6wUeQryoWQV7+Vhb44xzl2jMvc7R7ic4W9zxM0Oc57gxVeMP6Rfbr10+vMjBU+wvO8+XXq1dPtttuO+1PeYX2VzynCkv9c7Lk6fnOPK17np2TzcKUp+cP87RbVV7z5llYu/Iq5TVrZue/06qfd9cbWOZZB9R0UsOGYnnTSXmNG4mOmEsnS0bntTaWunXSt8hoLdMvbGcdfWDdkxpIw7Qy15FNtAtL9rZXWiuzdmbrOmPdy6K7cFVlORsyj9Wvrtpna2quW1P9NLel2qpeSx819F86ybpRWberdJJttSaYiWTdsOxBqrqA/vzoGIT08tvQWutGlc7Plv96183Cuh+lkxo1Ku8ul06eTM5rfumuUybrR1nVL1ApAGmoO2SZTtwJPdPilIcAAggggAACCCCAwJ8jkN5hnA1UR7sT+mC94o5e8levXKFH53VUW/Sd0IcPH76BSmaxCCCAAAIIIIAAAgggkEmBrAhAgjuhx1px7oQeS4VpCCCAAAIIIIAAAgiEU6BSF6xYq7FAb0G6mV5LreJlw2LNW5Vpdj3knj176vW07xG790gj7ay4XO/r8JVeTLtEL/A8atSoqiyWPAgggAACCCCAAAIIIJBlAnEDEL0rtegdyUWvHuUHhd95550yaNAgfz+O5tU8gNluMmg3O5yod8yZqxfstoDHyujVq5d069bNd8nKMjeqgwACCCCAAAIIVFnABp9n8fVMqrxemczYrl16g/gzWTfKSiwQNwB56KGH5L333pOXX35Zjj/+eDn44IP1RiyviU2/9tprEy+1Cu/GuxN6FRZFFgQQyEGBLaW1XCt35OCasUoIILAxCthd1oM7refS+m+xReYCq759c0lu41qXuGNAxo8fL1deeaVsueWWXsQuv9unTx8flGxcRBlaW72069rblW7wAms88Yjkrb27+QYvLBcL0G/XGhdfkNVrVvPJR7O6flWpnF1yNtsvFbq56C8vCYGQCeyjdz+/VK4LWa2pbrYK/O1vss5d5LO1ntTrzxWIG4C0atVKLAiJTq+++qpsYaEtKa6A3YOhxr13xn0/3hs1by6UvLY7xnu7WqfntWhRrcvb2BaWZ/cb2WP3rF7tvNats7p+uVq5e2Vorq5azq3XbrKP7CKdcm69qrpCeZJX1azkQwABBNIWiNsF67LLLpO99tpL3n77bb1Rzk/SpUsXPz7jnXfeSbuQjS1DXvtdN7ZVrt71bd1Kb5S4WfUuM87Salx9uegVFuK8y2QEEMhVAbuxJSm3BXbaSaRJk9xeR9YOgbAKxA1AWuhR8unTp8vzzz8v33//vRx44IH+UdNuw0lCYAMK1Dju2CotPe+yS9POl7f11mnnIQMCCCCAQPYLnH129tcxUzW0wdp6izUSAlkjEDcAGThwoKxatUr++te/SsuWLbOmwlQEgXgCNY49Ot5bTEcAAQT+NIF9dYxFc+F39E9rgBwruGvX9FcoFwe7p69AjmwSiDsG5IADDpCvv/5a7CaBxxxzjL8aVlFRUTbVnbogECqBmqNek7xNNglVnXOhsv+Ru2QTqZe1q1JT8vVf7p1Z/rfcpuoNstY9kxU7SHro5Qk425pJ81wuS++QQEIg9AJxA5COHTv6e378+OOP/izIE088IW3atBE7M0JCAAEEwiLQWtpIDf2XrelUOU+OkOPTrt4zkt3j8baR7TWsSi+wqi11pL40TNuiuxyVdp5czdBYNs3q7V3vNSzbbpu9+nqNETn00PTrN2wYwwnTVyPHxiwQtwtWgFJaWip25sP+2p3QN9Td0IPy+JuGwObNJe/Uk9PIwKwIIIBA9grsIV3FHummC0QvJpGh1FMuzEhJ1mXrakn/UPetMjgj9atqIQcdVNWcmcnXSK+If+65mSmLUv4QaNZM5I47/njNs9wXiBuAfPLJJ3LPPffIyJEj5SD9xrjwwgvlqKOOIgDJom0ib9NNJa/7QVlUI6qCAAII5LbA0XJKRlbQOua1kPL7cGWkwCoUcvXVVchEFgRiCNj1jbh6fAyYHJ4UNwD5+OOPZffdd5e7775bNt988xwmYNUQQAABBOIJ5GmHHusaRUKgooAekyTluEC97B0+l3F5PSZPqkaBuAFIr169qrEYFoUAAgggEEYBGzx9swwKY9WpMwIIrKfAqFHruYAcyt65cw6tTBasSqUAZB+9Vtvtt98uEyZMkCFDhlSqYo8ePaR///6VpjMBgVgCNc7+a6zJTEMAAQQQQAABBBDYSAUqBSAPPfSQXqFiW7EbEXbv3l1q1aoVoVmzZo0UFxdHXvMEgWQCNc4+M9ksvJ8lAnnWVtxo1LdGI72SUEsum5olWybVQEBEL8xJQqBaBBrqhfYYb1ItlOu1kEoBSDu9XWZZWZkMGjRIjjzySB+EBCUMHz5chg4d6u+IHkzjLwIIbDiBvB30mpV2XcgMpLwMlZOBVVnvIjrKnmIPEgIIZIfAgAHZUQ9qEX6BDh1E7EH6cwUqBSCPPfaYBOM/BlT4xDfUsPG///3vn1tjSv9TBPJ2303EdoZJGRXIs4vm2yOL02XSL+3anSxn6/V9WqWdjwzrJ3CinKlndrZav4WQGwEEEEAAgfUUyHOaKi6jpKRELPjo2rWr7Lln+VHAvLw87Z1Rs+KsoXk9TO8StGjRIundu3do6kxFEUAAAQQQQAABBBDINYFKZ0BsBe1mg5dfnrkbO+UaKuuDAAIIIIAAAggggAACsQUqBSBcBSs2FFMRQAABBBBAAAEEEEBg/QUqBSDBVbC2137nhx9+eKUSmjRpUmkaExBAAAEEEEAAAQQQQACBVAQqBSCdOnXy+Ro3biw27sPugr569Wqxwemt9bple+yxRyrLZR4EEEAAAQQQQAABBBBAoJJApQAkmMNuRHjYYYfJzJkzpbCwUCZPnixFRUWyZMkSOe+884LZ+IsAAggggAACCCCAAAIIpCxQI96cTz31lDzyyCP+hoQvvPCCPPnkk2LTXnzxxXhZmI4AAggggAACCCCAAAIIJBSIG4AsX75cmjdvLuPHj/fdsNq3by92J/RGjRolXCBvIoAAAggggAACCCCAAALxBOJ2wSooKJA+ffpIaWmpnH322TJ9+nQ566yz5Nprr423LKYjgAACCCCAAAIIIIAAAgkF4gYgPXv2lM0220yWLVsmJ510knz77bcyaNAg6d69e8IF8iYCCCCAAAIIIIAAAgggEE8gbgBiGewyvIsXL5apU6fKjjvuKDvssEO85TAdAQQQQAABBBBAAAEEEEgqEHcMiHW9Ouecc/xZkIMPPlgaNmwoJ554oh8HknSpzIAAAggggAACCCCAAAIIxBCIG4AMHjxYZs+e7cd+2FmQWbNmiXNObrvtthiLYRICCCCAAAIIIIAAAgggkFwgbgAyadIkueqqq6Rdu3Z+KW3atJHrr79exo0bl3ypzIEAAggggAACCCCAAAIIxBCIG4B07drVX4I3Oo9dktcuzUtCAAEEEEAAAQQQQAABBKoiEHcQ+sknnywdOnTwZzz2339/+eSTT+Szzz7jDEhVlMmDAAIIIIAAAggggAACXiDuGZBmzZr5q1+dccYZUlZWJnZfkGnTpsluu+0GHQIIIIAAAggggAACCCBQJYGYZ0AmT54s//vf/+SII46QSy+9tEoLJhMCCCCAAAIIIIAAAgggUFGg0hmQJ598Ug444AAZOnSo7LrrrvLPf/6zYh5eI4AAAggggAACCCCAAAJVEqgUgNx3333y1FNPyYQJE+Sjjz6Su+66i3t/VImWTAgggAACCCCAAAIIIFBRoFIAMm/ePNlnn338fB07dvRXvfrhhx8q5uM1AggggAACCCCAAAIIIJC2QKUApKioSGrVqhVZUJMmTWT16tWR1zxBAAEEEEAAAQQQQAABBKoqEHMQ+ooVK6ROnTp+maWlpbJy5UpZtmyZf127dm2pV69eVcsjHwIIIIAAAggggAACCGzEAjEDkLZt265DYjclDJLdH+SFF14IXvIXAQQQQAABBBBAAAEEEEhZoFIAMmvWLHHOxV2AnQEhIYAAAggggAACCCCAAAJVEagUgNiYDxICCCCAAAIIIIAAAgggsCEEKg1C3xCFsEwEEEAAAQQQQAABBBBAwAQIQNgOEEAAAQQQQAABBBBAIGMCBCAZo6YgBBBAAAEEEEAAAQQQIABhG0AAAQQQQAABBBBAAIGMCRCAZIyaghBAAAEEEEAAAQQQQIAAhG0AAQQQQAABBBBAAAEEMiZAAJIxagpCAAEEEEAAAQQQQAABAhC2AQQQQAABBBBAAAEEEMiYAAFIxqgpCAEEEEAAAQQQQAABBAhA2AYQQAABBBBAAAEEEEAgYwIEIBmjpiAEEEAAAQQQQAABBBAgAGEbQ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" alt="Figure A4, panel c: Positive Biographical Feedback" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel c: Positive Biographical Feedback</strong></p>
</div>
<div class="figure">
<img 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" alt="Figure A4, panel d: Positive Editorializing" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel d: Positive Editorializing</strong></p>
</div>
<div class="figure">
<img 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" alt="Figure A4, panel e: Praised Rental Qualifications" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel e: Praised Rental Qualifications</strong></p>
</div>
<div class="figure">
<img 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" alt="Figure A4, panel f: Sales Efforts" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel f: Sales Efforts</strong></p>
</div>
<div class="figure">
<img 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" alt="Figure A4, panel g: Professionalism" style="width:60.0%" />
<p class="caption"><strong>Figure A4, panel g: Professionalism</strong></p>
</div>
<pre class="r"><code>### The following analysis corresponds to the analyses described in footnote 22 in the article.
ctx$weekday &lt;- as.factor(weekdays(as.Date(ctx$sfa_B4_apptdate, format=&quot;%m/%d/%y&quot;)))
dat_tid &lt;- dat[,c(&quot;cid&quot;,&quot;TA&quot;,names(dat)[grepl(&quot;tid&quot;,names(dat))])]
dat_tid &lt;- cbind(dat_tid[,c(&quot;cid&quot;,&quot;TA&quot;)], t(apply(dat_tid[,3:ncol(dat_tid)], 1, function(J) names(dat_tid[,3:ncol(dat_tid)])[which(J==1)])))

dat_tid_1 &lt;- as.data.frame(dat_tid[,c(1,2,3)], stringsAsFactors=FALSE)
dat_tid_2 &lt;- as.data.frame(dat_tid[,c(1,2,4)], stringsAsFactors=FALSE)
dat_tid_3 &lt;- as.data.frame(dat_tid[,c(1,2,5)], stringsAsFactors=FALSE)

dat_tid_1[,3] &lt;- as.character(dat_tid_1[,3])
dat_tid_2[,3] &lt;- as.character(dat_tid_2[,3])
dat_tid_3[,3] &lt;- as.character(dat_tid_3[,3])
names(dat_tid_1)[3] &lt;- names(dat_tid_2)[3] &lt;- names(dat_tid_3)[3] &lt;- &quot;tid&quot;
dat_tid &lt;- rbind(dat_tid_1, dat_tid_2, dat_tid_3)
dat_tid$tid &lt;- gsub(&quot;tid.&quot;,&quot;&quot;,dat_tid$tid,fixed=TRUE)
dat_tid$ttype &lt;- substr(dat_tid$tid, 1, 1)

dat_sub &lt;- dat[,c(&quot;cid&quot;,&quot;TA&quot;,&quot;team_gender&quot;,&quot;callorder&quot;,&quot;partnered&quot;,&quot;numbr&quot;,
       paste0(rep(c(&quot;anyskep&quot;,&quot;anyneg&quot;,&quot;cb&quot;,&quot;off&quot;,&quot;prof&quot;,&quot;posedit&quot;,&quot;posbg&quot;,&quot;qualpraise&quot;,&quot;sales&quot;), 3),
              rep(c(&quot;_A&quot;,&quot;_B&quot;,&quot;_C&quot;), each=9))
       )]

dmelt &lt;- melt(dat_sub, id.vars=c(&quot;cid&quot;, &quot;TA&quot;, &quot;team_gender&quot;, &quot;callorder&quot;, &quot;partnered&quot;, &quot;numbr&quot;))
dmelt$ttype &lt;- ifelse(grepl(&quot;_A&quot;, dmelt$variable, fixed=TRUE), &quot;A&quot;,
                      ifelse(grepl(&quot;_B&quot;, dmelt$variable, fixed=TRUE), &quot;B&quot;, &quot;C&quot;))
dmelt$variable &lt;- gsub(&quot;_A&quot;, &quot;&quot;, dmelt$variable, fixed=TRUE)
dmelt$variable &lt;- gsub(&quot;_B&quot;, &quot;&quot;, dmelt$variable, fixed=TRUE)
dmelt$variable &lt;- gsub(&quot;_C&quot;, &quot;&quot;, dmelt$variable, fixed=TRUE)
dat_sub2 &lt;- dcast(dmelt, cid + TA + team_gender + callorder + partnered + numbr + ttype ~ variable, value.var=&quot;value&quot;)
dat2 &lt;- join(dat_tid, dat_sub2, by=c(&quot;cid&quot;,&quot;TA&quot;,&quot;ttype&quot;), type=&quot;left&quot;, match=&quot;all&quot;)

ctx_sub &lt;- ctx[,c(&quot;cid&quot;,&quot;weekday&quot;)]
ctx_sub &lt;- ctx_sub[!duplicated(ctx_sub$cid),]
#ctx[ctx$cid %in% ctx_sub$cid[is.na(ctx_sub$weekday)] &amp; !is.na(ctx$weekday),c(&quot;cid&quot;,&quot;weekday&quot;)]
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 11108, &quot;Wednesday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 12116,    &quot;Friday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 12608,    &quot;Monday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 13361,    &quot;Monday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 15734,    &quot;Friday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 19446,  &quot;Thursday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 26324,    &quot;Friday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(ctx_sub$cid == 30165,    &quot;Friday&quot;, ctx_sub$weekday)
ctx_sub$weekday &lt;- ifelse(is.na(ctx_sub$weekday), &quot;None&quot;, ctx_sub$weekday)

dat3 &lt;- join(dat2, ctx_sub, by=&quot;cid&quot;, type=&quot;left&quot;, match=&quot;all&quot;)

model.callback.0 &lt;- lmer(cb ~ (1|tid) + (1|ttype) + callorder + weekday + 
                         partnered + numbr + anyskep + anyneg + (1|team_gender), 
                       data=dat3, subset=(TA==0))

model.offer.0 &lt;- lmer(off ~ (1|tid) + (1|ttype) + callorder + weekday + 
                      partnered + numbr + anyskep + anyneg + (1|team_gender), 
                    data=dat3, subset=(TA==0))

model.callback.all &lt;- lmer(cb ~ (1|tid) + (1|ttype) + callorder + weekday + 
                           partnered + numbr + anyskep + anyneg + (1|team_gender), 
                         data=dat3)

model.offer.all &lt;- lmer(off ~ (1|tid) + (1|ttype) + callorder + weekday + 
                        partnered + numbr + anyskep + anyneg + (1|team_gender), 
                      data=dat3)

re_out &lt;- list(model.callback.0   , 
               model.offer.0      ,
               model.callback.all ,
               model.offer.all    )

re_table &lt;- lapply(re_out, function(J){
  x &lt;- as.data.frame(VarCorr(J))
  x[,4] &lt;- round(x[,4], 4)
  x &lt;- x[,c(1,4)]
  return(x)
})

re_table &lt;- do.call(cbind, re_table)
re_table &lt;- re_table[,-c(3,5,7)]
re_table[,1] &lt;- c(&quot;Estimated Variance of Varying Tester Intercepts&quot;,
                  &quot;Estimated Variance of Varying Tester Race Intercepts&quot;,
                  &quot;Estimated Variance of Varying Tester Team Gender Intercepts&quot;,
                  &quot;Estimated Residual Variance&quot;)
colnames(re_table) &lt;- c(&quot;&quot;, &quot;Control Group, Outcome: Callback&quot;, &quot;Control Group, Outcome: Offer&quot;,
                        &quot;Experimental Sample, Outcome: Callback&quot;, &quot;Experimental Sample, Outcome: Offer&quot;)

kable(re_table)</code></pre>
<table>
<thead>
<tr class="header">
<th></th>
<th align="right">Control Group, Outcome: Callback</th>
<th align="right">Control Group, Outcome: Offer</th>
<th align="right">Experimental Sample, Outcome: Callback</th>
<th align="right">Experimental Sample, Outcome: Offer</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Estimated Variance of Varying Tester Intercepts</td>
<td align="right">0.0034</td>
<td align="right">0.0046</td>
<td align="right">0.0035</td>
<td align="right">0.0048</td>
</tr>
<tr class="even">
<td>Estimated Variance of Varying Tester Race Intercepts</td>
<td align="right">0.0002</td>
<td align="right">0.0000</td>
<td align="right">0.0003</td>
<td align="right">0.0000</td>
</tr>
<tr class="odd">
<td>Estimated Variance of Varying Tester Team Gender Intercepts</td>
<td align="right">0.0010</td>
<td align="right">0.0000</td>
<td align="right">0.0010</td>
<td align="right">0.0000</td>
</tr>
<tr class="even">
<td>Estimated Residual Variance</td>
<td align="right">0.1408</td>
<td align="right">0.0759</td>
<td align="right">0.1352</td>
<td align="right">0.0740</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a4-p.a-20-selected-characteristics-of-housing-units-in-the-audit-and-experimental-samples" class="section level2">
<h2><span class="header-section-number">2.6</span> Table A4 (p. A-20): Selected Characteristics of Housing Units in the Audit and Experimental Samples</h2>
<p>We summarize selected <em>pre-treatment</em> characteristics of housing units in the audit and experimental samples. To ensure the measures are <em>pre-treatment</em> quantities, we summarize characteristics of housing units that are posted on and scraped from the original Craigslist ads.</p>
<pre class="r"><code># Helper functions and code to calculate descriptive statistics
stratum_labels &lt;- c(&quot;Total&quot;, &quot;Brooklyn&quot;, &quot;Bronx&quot;, &quot;Manhattan&quot;, &quot;Queens&quot;, &quot;Staten Island&quot;, &quot;Likely Discrimination Frame&quot;)

calc_mean_sds &lt;- function(df, x, label, digs=NULL) {
  
  x_mean &lt;- c(mean(df[[x]], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  x_sd &lt;- c(sd(df[[x]], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  if(is.null(digs)) digs &lt;- 2
  x_mean &lt;- sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_mean, digs) )
  x_sd &lt;- paste0(&quot;(&quot;, sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_sd, digs) ) ,&quot;)&quot;)
  
  x &lt;- cbind(x_mean, x_sd)
  colnames(x) &lt;- paste0(label, c(&quot;_mean&quot;, &quot;_sd&quot;))
  rownames(x) &lt;- NULL
  return(x)
}

calc_medians &lt;- function(df, x, label, digs=NULL) {
  
  x_med &lt;- c(median(df[[x]], na.rm=TRUE),
             median(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
             median(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
             median(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
             median(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
             median(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
             median(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  if(is.null(digs)) digs &lt;- 2
  x_med &lt;- sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_med, digs) )

  x &lt;- cbind(x_med, rep(&quot;&quot;, length(x_med)))
  colnames(x) &lt;- c(paste0(label, &quot;_median&quot;), &quot;&quot;)
  rownames(x) &lt;- NULL
  return(x)
}

calc_pct_ses &lt;- function(df, x, label, digs=NULL) {
  
  x_prop &lt;- c(mean(df[[x]], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
              mean(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  x_sd &lt;- c(sd(df[[x]], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
            sd(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  x_n &lt;- c(sum(!is.na(df[[x]])),
           sum(!is.na(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ])),
           sum(!is.na(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ])),
           sum(!is.na(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ])),
           sum(!is.na(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ])),
           sum(!is.na(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ])),
           sum(!is.na(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ]))
           )
  
  x_se &lt;- x_sd / sqrt(x_n)
  
  if(is.null(digs)) digs &lt;- 2
  x_pct &lt;- sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_prop * 100, digs) )
  x_se &lt;- paste0(&quot;(&quot;, sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_se * 100, digs) ) ,&quot;)&quot;)
  
  x &lt;- cbind(x_pct, x_se)
  colnames(x) &lt;- paste0(label, c(&quot;_p&quot;, &quot;_se&quot;))
  rownames(x) &lt;- NULL
  return(x)
}

calc_min_max &lt;- function(df, x, label, digs=NULL) {
  
  x_min &lt;- c(min(df[[x]], na.rm=TRUE),
              min(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
              min(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
              min(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
              min(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
              min(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
              min(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  x_max &lt;- c(max(df[[x]], na.rm=TRUE),
            max(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brk&quot; ], na.rm=TRUE),
            max(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;brx&quot; ], na.rm=TRUE),
            max(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;mnh&quot; ], na.rm=TRUE),
            max(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;que&quot; ], na.rm=TRUE),
            max(df[[x]][ df[[&quot;frame&quot;]] == &quot;representative&quot; &amp; df[[&quot;boro&quot;]] == &quot;stn&quot; ], na.rm=TRUE),
            max(df[[x]][ df[[&quot;frame&quot;]] != &quot;representative&quot; ], na.rm=TRUE))
  
  if(is.null(digs)) digs &lt;- 2
  x_min &lt;- sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_min, digs) )
  x_max &lt;- sprintf( paste0(&quot;%.&quot;,digs,&quot;f&quot;) , round(x_max, digs) )
  
  x &lt;- cbind(x_min, x_max)
  colnames(x) &lt;- paste0(label, c(&quot;_min&quot;, &quot;_max&quot;))
  rownames(x) &lt;- NULL
  return(x)
}

#----------------------------------------#
# Panel A: Number of units

panel_a_aud_n &lt;- c(nrow(aud),
                   table(aud$boro[aud$frame == &quot;representative&quot;]),
                   nrow(aud[aud$frame != &quot;representative&quot;,]))
panel_a_aud_p &lt;- paste0(&quot;(&quot;, sprintf(&quot;%.2f&quot;, round(panel_a_aud_n / nrow(aud) * 100, 2))  ,&quot;)&quot;)

panel_a_exp_n &lt;- c(nrow(dat),
                   table(dat$boro[dat$frame == &quot;representative&quot;]),
                   nrow(dat[dat$frame != &quot;representative&quot;,]))
panel_a_exp_p &lt;- paste0(&quot;(&quot;, sprintf(&quot;%.2f&quot;, round(panel_a_exp_n / nrow(dat) * 100, 2))  ,&quot;)&quot;)

panel_a_trt_n &lt;- c(nrow(dat[dat$TA != 0,]),
                   table(dat$boro[dat$frame == &quot;representative&quot; &amp; dat$TA != 0]),
                   nrow(dat[dat$frame != &quot;representative&quot; &amp; dat$TA != 0,]))
panel_a_trt_p &lt;- paste0(&quot;(&quot;, sprintf(&quot;%.2f&quot;, round(panel_a_trt_n / nrow(dat[dat$TA != 0,]) * 100, 2)) ,&quot;)&quot;)

panel_a_con_n &lt;- c(nrow(dat[dat$TA == 0,]),
                   table(dat$boro[dat$frame == &quot;representative&quot; &amp; dat$TA == 0]),
                   nrow(dat[dat$frame != &quot;representative&quot; &amp; dat$TA == 0,]))
panel_a_con_p &lt;- paste0(&quot;(&quot;, sprintf(&quot;%.2f&quot;, round(panel_a_con_n / nrow(dat[dat$TA == 0,]) * 100, 2)) ,&quot;)&quot;)

tab_a4_panel_a &lt;- cbind(stratum_labels,
                        panel_a_aud_n,
                        panel_a_aud_p,
                        panel_a_exp_n,
                        panel_a_exp_p,
                        panel_a_trt_n,
                        panel_a_trt_p,
                        panel_a_con_n,
                        panel_a_con_p)

rownames(tab_a4_panel_a) &lt;- NULL
colnames(tab_a4_panel_a) &lt;- gsub(&quot;panel_a_&quot;, &quot;&quot;, colnames(tab_a4_panel_a), fixed=TRUE)

kable(tab_a4_panel_a, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (N)&quot;, &quot;Audit Sample (%)&quot;,
                                  &quot;Exp Sample (N)&quot;, &quot;Exp Sample (%)&quot;,
                                  &quot;Any Treatment (N)&quot;, &quot;Any Treatment (%)&quot;,
                                  &quot;Control (N)&quot;, &quot;Control (%)&quot;),
      caption=&quot;**Table A4, Panel A. Number of Units (Scraped from Craigslist).**&quot;)</code></pre>
<table>
<caption><strong>Table A4, Panel A. Number of Units (Scraped from Craigslist).</strong></caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (N)</th>
<th align="left">Audit Sample (%)</th>
<th align="left">Exp Sample (N)</th>
<th align="left">Exp Sample (%)</th>
<th align="left">Any Treatment (N)</th>
<th align="left">Any Treatment (%)</th>
<th align="left">Control (N)</th>
<th align="left">Control (%)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">2711</td>
<td align="left">(100.00)</td>
<td align="left">653</td>
<td align="left">(100.00)</td>
<td align="left">374</td>
<td align="left">(100.00)</td>
<td align="left">279</td>
<td align="left">(100.00)</td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">801</td>
<td align="left">(29.55)</td>
<td align="left">208</td>
<td align="left">(31.85)</td>
<td align="left">118</td>
<td align="left">(31.55)</td>
<td align="left">90</td>
<td align="left">(32.26)</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">337</td>
<td align="left">(12.43)</td>
<td align="left">68</td>
<td align="left">(10.41)</td>
<td align="left">37</td>
<td align="left">(9.89)</td>
<td align="left">31</td>
<td align="left">(11.11)</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">668</td>
<td align="left">(24.64)</td>
<td align="left">216</td>
<td align="left">(33.08)</td>
<td align="left">128</td>
<td align="left">(34.22)</td>
<td align="left">88</td>
<td align="left">(31.54)</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">495</td>
<td align="left">(18.26)</td>
<td align="left">92</td>
<td align="left">(14.09)</td>
<td align="left">54</td>
<td align="left">(14.44)</td>
<td align="left">38</td>
<td align="left">(13.62)</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">254</td>
<td align="left">(9.37)</td>
<td align="left">25</td>
<td align="left">(3.83)</td>
<td align="left">12</td>
<td align="left">(3.21)</td>
<td align="left">13</td>
<td align="left">(4.66)</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">156</td>
<td align="left">(5.75)</td>
<td align="left">44</td>
<td align="left">(6.74)</td>
<td align="left">25</td>
<td align="left">(6.68)</td>
<td align="left">19</td>
<td align="left">(6.81)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#----------------------------------------#
# Panel B: Monthly asking price (mean)

tab_a4_panel_b &lt;- cbind(stratum_labels,
                    calc_mean_sds(df=aud, x=&quot;rent&quot;, label=&quot;aud&quot;),
                    calc_mean_sds(df=dat, x=&quot;rent&quot;, label=&quot;exp&quot;),
                    calc_mean_sds(df=dat[dat$TA != 0,], x=&quot;rent&quot;, label=&quot;trt&quot;),
                    calc_mean_sds(df=dat[dat$TA == 0,], x=&quot;rent&quot;, label=&quot;con&quot;))

kable(tab_a4_panel_b, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (Mean)&quot;, &quot;Audit Sample (SD)&quot;,
                                  &quot;Exp Sample (Mean)&quot;, &quot;Exp Sample (SD)&quot;,
                                  &quot;Any Treatment (Mean)&quot;, &quot;Any Treatment (SD)&quot;,
                                  &quot;Control (Mean)&quot;, &quot;Control (SD)&quot;),
      caption=&quot;**Table A4, Panel B. Monthly Asking Rental Price ($, Scraped from Craigslist).**&quot;)</code></pre>
<table>
<caption><strong>Table A4, Panel B. Monthly Asking Rental Price ($, Scraped from Craigslist).</strong></caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (Mean)</th>
<th align="left">Audit Sample (SD)</th>
<th align="left">Exp Sample (Mean)</th>
<th align="left">Exp Sample (SD)</th>
<th align="left">Any Treatment (Mean)</th>
<th align="left">Any Treatment (SD)</th>
<th align="left">Control (Mean)</th>
<th align="left">Control (SD)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">2340.00</td>
<td align="left">(4721.74)</td>
<td align="left">2429.74</td>
<td align="left">(1208.02)</td>
<td align="left">2411.23</td>
<td align="left">(1163.67)</td>
<td align="left">2456.26</td>
<td align="left">(1271.73)</td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">2190.57</td>
<td align="left">(1049.32)</td>
<td align="left">2319.49</td>
<td align="left">(956.68)</td>
<td align="left">2285.06</td>
<td align="left">(854.67)</td>
<td align="left">2370.20</td>
<td align="left">(1096.14)</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">2345.55</td>
<td align="left">(13616.75)</td>
<td align="left">1545.64</td>
<td align="left">(688.14)</td>
<td align="left">1506.96</td>
<td align="left">(675.92)</td>
<td align="left">1596.53</td>
<td align="left">(719.20)</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">3251.67</td>
<td align="left">(1600.49)</td>
<td align="left">3162.99</td>
<td align="left">(1447.25)</td>
<td align="left">3133.90</td>
<td align="left">(1404.18)</td>
<td align="left">3206.12</td>
<td align="left">(1520.28)</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">1718.19</td>
<td align="left">(562.32)</td>
<td align="left">1885.07</td>
<td align="left">(557.62)</td>
<td align="left">1846.75</td>
<td align="left">(449.90)</td>
<td align="left">1940.82</td>
<td align="left">(693.15)</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">1383.49</td>
<td align="left">(511.19)</td>
<td align="left">1335.62</td>
<td align="left">(596.31)</td>
<td align="left">1368.89</td>
<td align="left">(655.17)</td>
<td align="left">1292.86</td>
<td align="left">(559.34)</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">2479.07</td>
<td align="left">(1451.93)</td>
<td align="left">2321.16</td>
<td align="left">(736.25)</td>
<td align="left">2336.08</td>
<td align="left">(778.38)</td>
<td align="left">2301.53</td>
<td align="left">(697.40)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#----------------------------------------#
# Panel C: Monthly asking price (median)

tab_a4_panel_c &lt;- cbind(stratum_labels,
                        calc_medians(df=aud, x=&quot;rent&quot;, label=&quot;aud&quot;),
                        calc_medians(df=dat, x=&quot;rent&quot;, label=&quot;exp&quot;),
                        calc_medians(df=dat[dat$TA != 0,], x=&quot;rent&quot;, label=&quot;trt&quot;),
                        calc_medians(df=dat[dat$TA == 0,], x=&quot;rent&quot;, label=&quot;con&quot;))

kable(tab_a4_panel_c, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (Median)&quot;, &quot;&quot;,
                                  &quot;Exp Sample (Median)&quot;, &quot;&quot;,
                                  &quot;Any Treatment (Median)&quot;, &quot;&quot;,
                                  &quot;Control (Median)&quot;, &quot;&quot;),
      caption=&quot;**Table A4, Panel C. Median Monthly Asking Rental Price ($, Scraped from Craigslist).**&quot;)</code></pre>
<table>
<caption><strong>Table A4, Panel C. Median Monthly Asking Rental Price ($, Scraped from Craigslist).</strong></caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (Median)</th>
<th></th>
<th align="left">Exp Sample (Median)</th>
<th></th>
<th align="left">Any Treatment (Median)</th>
<th></th>
<th align="left">Control (Median)</th>
<th></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">1850.00</td>
<td></td>
<td align="left">2197.50</td>
<td></td>
<td align="left">2200.00</td>
<td></td>
<td align="left">2025.00</td>
<td></td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">1983.50</td>
<td></td>
<td align="left">2200.00</td>
<td></td>
<td align="left">2299.00</td>
<td></td>
<td align="left">2050.00</td>
<td></td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">1325.00</td>
<td></td>
<td align="left">1400.00</td>
<td></td>
<td align="left">1400.00</td>
<td></td>
<td align="left">1400.00</td>
<td></td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">2950.00</td>
<td></td>
<td align="left">2897.50</td>
<td></td>
<td align="left">2900.00</td>
<td></td>
<td align="left">2872.50</td>
<td></td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">1600.00</td>
<td></td>
<td align="left">1850.00</td>
<td></td>
<td align="left">1850.00</td>
<td></td>
<td align="left">1850.00</td>
<td></td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">1300.00</td>
<td></td>
<td align="left">1100.00</td>
<td></td>
<td align="left">1195.00</td>
<td></td>
<td align="left">1100.00</td>
<td></td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">2272.50</td>
<td></td>
<td align="left">2249.50</td>
<td></td>
<td align="left">2200.00</td>
<td></td>
<td align="left">2300.00</td>
<td></td>
</tr>
</tbody>
</table>
<pre class="r"><code>#----------------------------------------#
# Panel D: Number of bedrooms

tab_a4_panel_d &lt;- cbind(stratum_labels,
                        calc_mean_sds(df=aud, x=&quot;numbr&quot;, label=&quot;aud&quot;),
                        calc_mean_sds(df=dat, x=&quot;numbr&quot;, label=&quot;exp&quot;),
                        calc_mean_sds(df=dat[dat$TA != 0,], x=&quot;numbr&quot;, label=&quot;trt&quot;),
                        calc_mean_sds(df=dat[dat$TA == 0,], x=&quot;numbr&quot;, label=&quot;con&quot;))

kable(tab_a4_panel_d, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (Mean)&quot;, &quot;Audit Sample (SD)&quot;,
                                  &quot;Exp Sample (Mean)&quot;, &quot;Exp Sample (SD)&quot;,
                                  &quot;Any Treatment (Mean)&quot;, &quot;Any Treatment (SD)&quot;,
                                  &quot;Control (Mean)&quot;, &quot;Control (SD)&quot;),
      caption=&quot;**Table A4, Panel D. Number of Bedrooms (Scraped from Craigslist).**&quot;)</code></pre>
<table>
<caption><strong>Table A4, Panel D. Number of Bedrooms (Scraped from Craigslist).</strong></caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (Mean)</th>
<th align="left">Audit Sample (SD)</th>
<th align="left">Exp Sample (Mean)</th>
<th align="left">Exp Sample (SD)</th>
<th align="left">Any Treatment (Mean)</th>
<th align="left">Any Treatment (SD)</th>
<th align="left">Control (Mean)</th>
<th align="left">Control (SD)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">0.94</td>
<td align="left">(1.22)</td>
<td align="left">0.88</td>
<td align="left">(1.19)</td>
<td align="left">0.85</td>
<td align="left">(1.17)</td>
<td align="left">0.92</td>
<td align="left">(1.21)</td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">1.00</td>
<td align="left">(1.23)</td>
<td align="left">1.01</td>
<td align="left">(1.30)</td>
<td align="left">0.97</td>
<td align="left">(1.26)</td>
<td align="left">1.07</td>
<td align="left">(1.36)</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">0.82</td>
<td align="left">(1.20)</td>
<td align="left">0.85</td>
<td align="left">(1.16)</td>
<td align="left">0.86</td>
<td align="left">(1.23)</td>
<td align="left">0.84</td>
<td align="left">(1.10)</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">0.85</td>
<td align="left">(1.15)</td>
<td align="left">0.69</td>
<td align="left">(1.04)</td>
<td align="left">0.63</td>
<td align="left">(0.98)</td>
<td align="left">0.76</td>
<td align="left">(1.13)</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">0.81</td>
<td align="left">(1.15)</td>
<td align="left">0.59</td>
<td align="left">(0.90)</td>
<td align="left">0.56</td>
<td align="left">(0.90)</td>
<td align="left">0.63</td>
<td align="left">(0.91)</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">0.89</td>
<td align="left">(1.29)</td>
<td align="left">0.80</td>
<td align="left">(1.50)</td>
<td align="left">1.08</td>
<td align="left">(1.83)</td>
<td align="left">0.54</td>
<td align="left">(1.13)</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">1.88</td>
<td align="left">(1.23)</td>
<td align="left">1.95</td>
<td align="left">(1.01)</td>
<td align="left">1.92</td>
<td align="left">(1.00)</td>
<td align="left">2.00</td>
<td align="left">(1.05)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#----------------------------------------#
# Panel E: Square footage

tab_a4_panel_e &lt;- cbind(stratum_labels,
                        calc_mean_sds(df=aud, x=&quot;sqft&quot;, label=&quot;aud&quot;),
                        calc_mean_sds(df=dat, x=&quot;sqft&quot;, label=&quot;exp&quot;),
                        calc_mean_sds(df=dat[dat$TA != 0,], x=&quot;sqft&quot;, label=&quot;trt&quot;),
                        calc_mean_sds(df=dat[dat$TA == 0,], x=&quot;sqft&quot;, label=&quot;con&quot;))

kable(tab_a4_panel_e, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (Mean)&quot;, &quot;Audit Sample (SD)&quot;,
                                  &quot;Exp Sample (Mean)&quot;, &quot;Exp Sample (SD)&quot;,
                                  &quot;Any Treatment (Mean)&quot;, &quot;Any Treatment (SD)&quot;,
                                  &quot;Control (Mean)&quot;, &quot;Control (SD)&quot;),
      caption=&quot;**Table A4, Panel E. Square Footage (Scraped from Craigslist).** Standard deviation is noted as NA when only one observation in a given stratum has non-missing square footage data. Square footage information is rarely posted on Craigslist rental ads in New York City.&quot;)</code></pre>
<table>
<caption><strong>Table A4, Panel E. Square Footage (Scraped from Craigslist).</strong> Standard deviation is noted as NA when only one observation in a given stratum has non-missing square footage data. Square footage information is rarely posted on Craigslist rental ads in New York City.</caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (Mean)</th>
<th align="left">Audit Sample (SD)</th>
<th align="left">Exp Sample (Mean)</th>
<th align="left">Exp Sample (SD)</th>
<th align="left">Any Treatment (Mean)</th>
<th align="left">Any Treatment (SD)</th>
<th align="left">Control (Mean)</th>
<th align="left">Control (SD)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">1504.70</td>
<td align="left">(8050.42)</td>
<td align="left">1017.49</td>
<td align="left">(448.09)</td>
<td align="left">915.00</td>
<td align="left">(449.36)</td>
<td align="left">1116.68</td>
<td align="left">(430.93)</td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">3016.84</td>
<td align="left">(16292.51)</td>
<td align="left">1125.94</td>
<td align="left">(638.07)</td>
<td align="left">1085.11</td>
<td align="left">(661.49)</td>
<td align="left">1171.88</td>
<td align="left">(652.64)</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">999.59</td>
<td align="left">(352.42)</td>
<td align="left">1328.75</td>
<td align="left">(453.84)</td>
<td align="left">1100.00</td>
<td align="left">(NA)</td>
<td align="left">1405.00</td>
<td align="left">(523.52)</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">1029.82</td>
<td align="left">(495.06)</td>
<td align="left">926.79</td>
<td align="left">(350.32)</td>
<td align="left">777.43</td>
<td align="left">(334.36)</td>
<td align="left">1135.90</td>
<td align="left">(262.42)</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">930.39</td>
<td align="left">(774.61)</td>
<td align="left">917.60</td>
<td align="left">(260.29)</td>
<td align="left">866.67</td>
<td align="left">(288.68)</td>
<td align="left">994.00</td>
<td align="left">(291.33)</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">1203.25</td>
<td align="left">(981.19)</td>
<td align="left">1233.33</td>
<td align="left">(305.51)</td>
<td align="left">1300.00</td>
<td align="left">(NA)</td>
<td align="left">1200.00</td>
<td align="left">(424.26)</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">1011.50</td>
<td align="left">(568.51)</td>
<td align="left">885.00</td>
<td align="left">(272.45)</td>
<td align="left">900.00</td>
<td align="left">(141.42)</td>
<td align="left">880.00</td>
<td align="left">(315.91)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#----------------------------------------#
# Panel F: Listed by broker

tab_a4_panel_f &lt;- cbind(stratum_labels,
                        calc_pct_ses(df=aud, x=&quot;broker&quot;, label=&quot;aud&quot;),
                        calc_pct_ses(df=dat, x=&quot;broker&quot;, label=&quot;exp&quot;),
                        calc_pct_ses(df=dat[dat$TA != 0,], x=&quot;broker&quot;, label=&quot;trt&quot;),
                        calc_pct_ses(df=dat[dat$TA == 0,], x=&quot;broker&quot;, label=&quot;con&quot;))

kable(tab_a4_panel_f, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (%)&quot;, &quot;Audit Sample (SE)&quot;,
                                  &quot;Exp Sample (%)&quot;, &quot;Exp Sample (SE)&quot;,
                                  &quot;Any Treatment (%)&quot;, &quot;Any Treatment (SE)&quot;,
                                  &quot;Control (%)&quot;, &quot;Control (SE)&quot;),
      caption=&quot;**Table A4, Panel E. Listed by Broker (Scraped from Craigslist).**&quot;)</code></pre>
<table>
<caption><strong>Table A4, Panel E. Listed by Broker (Scraped from Craigslist).</strong></caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (%)</th>
<th align="left">Audit Sample (SE)</th>
<th align="left">Exp Sample (%)</th>
<th align="left">Exp Sample (SE)</th>
<th align="left">Any Treatment (%)</th>
<th align="left">Any Treatment (SE)</th>
<th align="left">Control (%)</th>
<th align="left">Control (SE)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">57.29</td>
<td align="left">(0.95)</td>
<td align="left">84.53</td>
<td align="left">(1.42)</td>
<td align="left">83.42</td>
<td align="left">(1.93)</td>
<td align="left">86.02</td>
<td align="left">(2.08)</td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">56.43</td>
<td align="left">(1.75)</td>
<td align="left">85.58</td>
<td align="left">(2.44)</td>
<td align="left">86.44</td>
<td align="left">(3.17)</td>
<td align="left">84.44</td>
<td align="left">(3.84)</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">51.04</td>
<td align="left">(2.73)</td>
<td align="left">75.00</td>
<td align="left">(5.29)</td>
<td align="left">72.97</td>
<td align="left">(7.40)</td>
<td align="left">77.42</td>
<td align="left">(7.63)</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">65.72</td>
<td align="left">(1.84)</td>
<td align="left">88.89</td>
<td align="left">(2.14)</td>
<td align="left">87.50</td>
<td align="left">(2.93)</td>
<td align="left">90.91</td>
<td align="left">(3.08)</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">51.72</td>
<td align="left">(2.25)</td>
<td align="left">81.52</td>
<td align="left">(4.07)</td>
<td align="left">79.63</td>
<td align="left">(5.53)</td>
<td align="left">84.21</td>
<td align="left">(5.99)</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">52.36</td>
<td align="left">(3.14)</td>
<td align="left">56.00</td>
<td align="left">(10.13)</td>
<td align="left">41.67</td>
<td align="left">(14.86)</td>
<td align="left">69.23</td>
<td align="left">(13.32)</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">64.74</td>
<td align="left">(3.84)</td>
<td align="left">95.45</td>
<td align="left">(3.18)</td>
<td align="left">92.00</td>
<td align="left">(5.54)</td>
<td align="left">100.00</td>
<td align="left">(0.00)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>#----------------------------------------#
# Min/Max Advertised Monthly Rental Price 
# (Reported in online SI text, page A-19)

min_max_monthly_rent &lt;- cbind(stratum_labels,
                              calc_min_max(df=aud, x=&quot;rent&quot;, label=&quot;aud&quot;),
                              calc_min_max(df=dat, x=&quot;rent&quot;, label=&quot;exp&quot;),
                              calc_min_max(df=dat[dat$TA != 0,], x=&quot;rent&quot;, label=&quot;trt&quot;),
                              calc_min_max(df=dat[dat$TA == 0,], x=&quot;rent&quot;, label=&quot;con&quot;))

kable(min_max_monthly_rent, col.names=c(&quot;Stratum&quot;, &quot;Audit Sample (Min)&quot;, &quot;Audit Sample (Max)&quot;,
                                        &quot;Exp Sample (Min)&quot;, &quot;Exp Sample (Max)&quot;,
                                        &quot;Any Treatment (Min)&quot;, &quot;Any Treatment (Max)&quot;,
                                        &quot;Control (Min)&quot;, &quot;Control (Max)&quot;),
      caption=&quot;**Mininum and Maximum Advertised Monthly Rental Price (Reported in online SI, page A-19)**&quot;)</code></pre>
<table>
<caption><strong>Mininum and Maximum Advertised Monthly Rental Price (Reported in online SI, page A-19)</strong></caption>
<thead>
<tr class="header">
<th align="left">Stratum</th>
<th align="left">Audit Sample (Min)</th>
<th align="left">Audit Sample (Max)</th>
<th align="left">Exp Sample (Min)</th>
<th align="left">Exp Sample (Max)</th>
<th align="left">Any Treatment (Min)</th>
<th align="left">Any Treatment (Max)</th>
<th align="left">Control (Min)</th>
<th align="left">Control (Max)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Total</td>
<td align="left">160.00</td>
<td align="left">200000.00</td>
<td align="left">160.00</td>
<td align="left">9495.00</td>
<td align="left">160.00</td>
<td align="left">9495.00</td>
<td align="left">850.00</td>
<td align="left">8900.00</td>
</tr>
<tr class="even">
<td align="left">Brooklyn</td>
<td align="left">175.00</td>
<td align="left">14000.00</td>
<td align="left">950.00</td>
<td align="left">6400.00</td>
<td align="left">1099.00</td>
<td align="left">5300.00</td>
<td align="left">950.00</td>
<td align="left">6400.00</td>
</tr>
<tr class="odd">
<td align="left">Bronx</td>
<td align="left">160.00</td>
<td align="left">200000.00</td>
<td align="left">160.00</td>
<td align="left">3999.00</td>
<td align="left">160.00</td>
<td align="left">3500.00</td>
<td align="left">898.00</td>
<td align="left">3999.00</td>
</tr>
<tr class="even">
<td align="left">Manhattan</td>
<td align="left">400.00</td>
<td align="left">13000.00</td>
<td align="left">1095.00</td>
<td align="left">9495.00</td>
<td align="left">1095.00</td>
<td align="left">9495.00</td>
<td align="left">1400.00</td>
<td align="left">8900.00</td>
</tr>
<tr class="odd">
<td align="left">Queens</td>
<td align="left">450.00</td>
<td align="left">5000.00</td>
<td align="left">835.00</td>
<td align="left">4000.00</td>
<td align="left">835.00</td>
<td align="left">2750.00</td>
<td align="left">1100.00</td>
<td align="left">4000.00</td>
</tr>
<tr class="even">
<td align="left">Staten Island</td>
<td align="left">600.00</td>
<td align="left">4000.00</td>
<td align="left">750.00</td>
<td align="left">2800.00</td>
<td align="left">750.00</td>
<td align="left">2800.00</td>
<td align="left">850.00</td>
<td align="left">2300.00</td>
</tr>
<tr class="odd">
<td align="left">Likely Discrimination Frame</td>
<td align="left">825.00</td>
<td align="left">15000.00</td>
<td align="left">1100.00</td>
<td align="left">4150.00</td>
<td align="left">1100.00</td>
<td align="left">4150.00</td>
<td align="left">1250.00</td>
<td align="left">3395.00</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 17:10:24 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lllllllll}
##   \hline
## V1 &amp; Audit Sample &amp; Audit Sample &amp; Experimental Sample &amp; Experimental Sample &amp; Any Treatment &amp; Any Treatment &amp; Control Group &amp; Control Group \\ 
##   \hline
## Panel A. Number of Units &amp; N &amp; Percent &amp; N &amp; Percent &amp; N &amp; Percent &amp; N &amp; Percent \\ 
##   Total &amp; 2711 &amp; (100.00) &amp; 653 &amp; (100.00) &amp; 374 &amp; (100.00) &amp; 279 &amp; (100.00) \\ 
##   Brooklyn &amp; 801 &amp; (29.55) &amp; 208 &amp; (31.85) &amp; 118 &amp; (31.55) &amp; 90 &amp; (32.26) \\ 
##   Bronx &amp; 337 &amp; (12.43) &amp; 68 &amp; (10.41) &amp; 37 &amp; (9.89) &amp; 31 &amp; (11.11) \\ 
##   Manhattan &amp; 668 &amp; (24.64) &amp; 216 &amp; (33.08) &amp; 128 &amp; (34.22) &amp; 88 &amp; (31.54) \\ 
##   Queens &amp; 495 &amp; (18.26) &amp; 92 &amp; (14.09) &amp; 54 &amp; (14.44) &amp; 38 &amp; (13.62) \\ 
##   Staten Island &amp; 254 &amp; (9.37) &amp; 25 &amp; (3.83) &amp; 12 &amp; (3.21) &amp; 13 &amp; (4.66) \\ 
##   Likely Discrimination Frame &amp; 156 &amp; (5.75) &amp; 44 &amp; (6.74) &amp; 25 &amp; (6.68) &amp; 19 &amp; (6.81) \\ 
##   Panel B. Monthly Asking Price (\$) &amp; Mean &amp; SD &amp; Mean &amp; SD &amp; Mean &amp; SD &amp; Mean &amp; SD \\ 
##   Total &amp; 2340.00 &amp; (4721.74) &amp; 2429.74 &amp; (1208.02) &amp; 2411.23 &amp; (1163.67) &amp; 2456.26 &amp; (1271.73) \\ 
##   Brooklyn &amp; 2190.57 &amp; (1049.32) &amp; 2319.49 &amp; (956.68) &amp; 2285.06 &amp; (854.67) &amp; 2370.20 &amp; (1096.14) \\ 
##   Bronx &amp; 2345.55 &amp; (13616.75) &amp; 1545.64 &amp; (688.14) &amp; 1506.96 &amp; (675.92) &amp; 1596.53 &amp; (719.20) \\ 
##   Manhattan &amp; 3251.67 &amp; (1600.49) &amp; 3162.99 &amp; (1447.25) &amp; 3133.90 &amp; (1404.18) &amp; 3206.12 &amp; (1520.28) \\ 
##   Queens &amp; 1718.19 &amp; (562.32) &amp; 1885.07 &amp; (557.62) &amp; 1846.75 &amp; (449.90) &amp; 1940.82 &amp; (693.15) \\ 
##   Staten Island &amp; 1383.49 &amp; (511.19) &amp; 1335.62 &amp; (596.31) &amp; 1368.89 &amp; (655.17) &amp; 1292.86 &amp; (559.34) \\ 
##   Likely Discrimination Frame &amp; 2479.07 &amp; (1451.93) &amp; 2321.16 &amp; (736.25) &amp; 2336.08 &amp; (778.38) &amp; 2301.53 &amp; (697.40) \\ 
##   Panel C. Median Monthly Asking Price (\$) &amp; Median &amp;  &amp; Median &amp;  &amp; Median &amp;  &amp; Median &amp;  \\ 
##   Total &amp; 1850.00 &amp;  &amp; 2197.50 &amp;  &amp; 2200.00 &amp;  &amp; 2025.00 &amp;  \\ 
##   Brooklyn &amp; 1983.50 &amp;  &amp; 2200.00 &amp;  &amp; 2299.00 &amp;  &amp; 2050.00 &amp;  \\ 
##   Bronx &amp; 1325.00 &amp;  &amp; 1400.00 &amp;  &amp; 1400.00 &amp;  &amp; 1400.00 &amp;  \\ 
##   Manhattan &amp; 2950.00 &amp;  &amp; 2897.50 &amp;  &amp; 2900.00 &amp;  &amp; 2872.50 &amp;  \\ 
##   Queens &amp; 1600.00 &amp;  &amp; 1850.00 &amp;  &amp; 1850.00 &amp;  &amp; 1850.00 &amp;  \\ 
##   Staten Island &amp; 1300.00 &amp;  &amp; 1100.00 &amp;  &amp; 1195.00 &amp;  &amp; 1100.00 &amp;  \\ 
##   Likely Discrimination Frame &amp; 2272.50 &amp;  &amp; 2249.50 &amp;  &amp; 2200.00 &amp;  &amp; 2300.00 &amp;  \\ 
##   Panel D. Number of Bedrooms &amp; Mean &amp; SD &amp; Mean &amp; SD &amp; Mean &amp; SD &amp; Mean &amp; SD \\ 
##   Total &amp; 0.94 &amp; (1.22) &amp; 0.88 &amp; (1.19) &amp; 0.85 &amp; (1.17) &amp; 0.92 &amp; (1.21) \\ 
##   Brooklyn &amp; 1.00 &amp; (1.23) &amp; 1.01 &amp; (1.30) &amp; 0.97 &amp; (1.26) &amp; 1.07 &amp; (1.36) \\ 
##   Bronx &amp; 0.82 &amp; (1.20) &amp; 0.85 &amp; (1.16) &amp; 0.86 &amp; (1.23) &amp; 0.84 &amp; (1.10) \\ 
##   Manhattan &amp; 0.85 &amp; (1.15) &amp; 0.69 &amp; (1.04) &amp; 0.63 &amp; (0.98) &amp; 0.76 &amp; (1.13) \\ 
##   Queens &amp; 0.81 &amp; (1.15) &amp; 0.59 &amp; (0.90) &amp; 0.56 &amp; (0.90) &amp; 0.63 &amp; (0.91) \\ 
##   Staten Island &amp; 0.89 &amp; (1.29) &amp; 0.80 &amp; (1.50) &amp; 1.08 &amp; (1.83) &amp; 0.54 &amp; (1.13) \\ 
##   Likely Discrimination Frame &amp; 1.88 &amp; (1.23) &amp; 1.95 &amp; (1.01) &amp; 1.92 &amp; (1.00) &amp; 2.00 &amp; (1.05) \\ 
##   Panel E. Square Footage &amp; Mean &amp; SD &amp; Mean &amp; SD &amp; Mean &amp; SD &amp; Mean &amp; SD \\ 
##   Total &amp; 1504.70 &amp; (8050.42) &amp; 1017.49 &amp; (448.09) &amp; 915.00 &amp; (449.36) &amp; 1116.68 &amp; (430.93) \\ 
##   Brooklyn &amp; 3016.84 &amp; (16292.51) &amp; 1125.94 &amp; (638.07) &amp; 1085.11 &amp; (661.49) &amp; 1171.88 &amp; (652.64) \\ 
##   Bronx &amp; 999.59 &amp; (352.42) &amp; 1328.75 &amp; (453.84) &amp; 1100.00 &amp; (NA) &amp; 1405.00 &amp; (523.52) \\ 
##   Manhattan &amp; 1029.82 &amp; (495.06) &amp; 926.79 &amp; (350.32) &amp; 777.43 &amp; (334.36) &amp; 1135.90 &amp; (262.42) \\ 
##   Queens &amp; 930.39 &amp; (774.61) &amp; 917.60 &amp; (260.29) &amp; 866.67 &amp; (288.68) &amp; 994.00 &amp; (291.33) \\ 
##   Staten Island &amp; 1203.25 &amp; (981.19) &amp; 1233.33 &amp; (305.51) &amp; 1300.00 &amp; (NA) &amp; 1200.00 &amp; (424.26) \\ 
##   Likely Discrimination Frame &amp; 1011.50 &amp; (568.51) &amp; 885.00 &amp; (272.45) &amp; 900.00 &amp; (141.42) &amp; 880.00 &amp; (315.91) \\ 
##   Panel F. Listed by Broker &amp; Percent &amp; SE &amp; Percent &amp; SE &amp; Percent &amp; SE &amp; Percent &amp; SE \\ 
##   Total &amp; 57.29 &amp; (0.95) &amp; 84.53 &amp; (1.42) &amp; 83.42 &amp; (1.93) &amp; 86.02 &amp; (2.08) \\ 
##   Brooklyn &amp; 56.43 &amp; (1.75) &amp; 85.58 &amp; (2.44) &amp; 86.44 &amp; (3.17) &amp; 84.44 &amp; (3.84) \\ 
##   Bronx &amp; 51.04 &amp; (2.73) &amp; 75.00 &amp; (5.29) &amp; 72.97 &amp; (7.40) &amp; 77.42 &amp; (7.63) \\ 
##   Manhattan &amp; 65.72 &amp; (1.84) &amp; 88.89 &amp; (2.14) &amp; 87.50 &amp; (2.93) &amp; 90.91 &amp; (3.08) \\ 
##   Queens &amp; 51.72 &amp; (2.25) &amp; 81.52 &amp; (4.07) &amp; 79.63 &amp; (5.53) &amp; 84.21 &amp; (5.99) \\ 
##   Staten Island &amp; 52.36 &amp; (3.14) &amp; 56.00 &amp; (10.13) &amp; 41.67 &amp; (14.86) &amp; 69.23 &amp; (13.32) \\ 
##   Likely Discrimination Frame &amp; 64.74 &amp; (3.84) &amp; 95.45 &amp; (3.18) &amp; 92.00 &amp; (5.54) &amp; 100.00 &amp; (0.00) \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a5-p.a-21-distribution-of-rental-units-across-boroughs-by-sample" class="section level2">
<h2><span class="header-section-number">2.7</span> Table A5 (p. A-21): Distribution of Rental Units Across Boroughs, by Sample</h2>
<pre class="r"><code># Note: Citywide 2011 numbers are from Table 5 in 
#  http://www.nyc.gov/html/hpd/downloads/pdf/HPD-2011-HVS-Selected-Findings-Tables.pdf
# For an archived version of this document available via the Internet Archive Wayback Machine, see
#  http://web.archive.org/web/20131102065647/http://www.nyc.gov/html/hpd/downloads/pdf/HPD-2011-HVS-Selected-Findings-Tables.pdf 
# A PDF copy of this document is also included in the replication archive

tab.city.n &lt;- c(691178, 388022, 587313, 449108, 57013)
tab.city.pct &lt;- c(31.81, 17.86, 27.03, 20.67, 2.62)

db.aud &lt;- ddply(aud[aud$frame != &quot;likely disc&quot;,], .(boro), summarise,
                num = length(cid),
                pct = round(length(cid)/nrow(aud[aud$frame != &quot;likely disc&quot;,])*100, 2))

db.exp &lt;- ddply(dat[dat$frame != &quot;likely disc&quot;,], .(boro), summarise,
                num = length(cid),
                pct = round(length(cid)/nrow(dat[dat$frame != &quot;likely disc&quot;,])*100, 2))

db.con &lt;- ddply(dat[dat$frame != &quot;likely disc&quot; &amp; dat$TA==0,], .(boro), summarise,
                num = length(cid),
                pct = round(length(cid)/nrow(dat[dat$frame != &quot;likely disc&quot; &amp; dat$TA==0,])*100, 2))

colnames(db.aud) &lt;- c(&quot;boro&quot;, &quot;Audit Sample (N)&quot;, &quot;Audit Sample (%)&quot;)
colnames(db.exp) &lt;- c(&quot;boro&quot;, &quot;Exp Sample (N)&quot;, &quot;Exp Sample (%)&quot;)
colnames(db.con) &lt;- c(&quot;boro&quot;, &quot;Control Group (N)&quot;, &quot;Control Group (%)&quot;)

tab.db &lt;- join(db.aud, db.exp, by=&quot;boro&quot;, type=&quot;left&quot;, match=&quot;all&quot;)
tab.db &lt;- join(tab.db, db.con, by=&quot;boro&quot;, type=&quot;left&quot;, match=&quot;all&quot;)

tab.db &lt;- cbind(tab.db[,1],
                tab.city.n,
                tab.city.pct,
                tab.db[,2:ncol(tab.db)])

tab.db[,1] &lt;- c(&quot;Brooklyn&quot;, &quot;Bronx&quot;, &quot;Manhattan&quot;, &quot;Queens&quot;, &quot;Staten Island&quot;)
colnames(tab.db)[1:3] &lt;- c(&quot;Borough&quot;, &quot;Citywide 2011 (N)&quot;, &quot;Citywide 2011 (%)&quot;)

tab.db &lt;- rbind(tab.db,
                c(&quot;Total&quot;, round(apply(tab.db[,2:ncol(tab.db)],2,sum), 0)))

print(xtable(tab.db), file=&quot;out_tablea5_dist_by_borough.tex&quot;, include.rownames=FALSE)

kable(tab.db, caption=&quot;**Table A5**&quot;)</code></pre>
<table>
<caption><strong>Table A5</strong></caption>
<thead>
<tr class="header">
<th align="left">Borough</th>
<th align="left">Citywide 2011 (N)</th>
<th align="left">Citywide 2011 (%)</th>
<th align="left">Audit Sample (N)</th>
<th align="left">Audit Sample (%)</th>
<th align="left">Exp Sample (N)</th>
<th align="left">Exp Sample (%)</th>
<th align="left">Control Group (N)</th>
<th align="left">Control Group (%)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Brooklyn</td>
<td align="left">691178</td>
<td align="left">31.81</td>
<td align="left">801</td>
<td align="left">31.35</td>
<td align="left">208</td>
<td align="left">34.15</td>
<td align="left">90</td>
<td align="left">34.62</td>
</tr>
<tr class="even">
<td align="left">Bronx</td>
<td align="left">388022</td>
<td align="left">17.86</td>
<td align="left">337</td>
<td align="left">13.19</td>
<td align="left">68</td>
<td align="left">11.17</td>
<td align="left">31</td>
<td align="left">11.92</td>
</tr>
<tr class="odd">
<td align="left">Manhattan</td>
<td align="left">587313</td>
<td align="left">27.03</td>
<td align="left">668</td>
<td align="left">26.14</td>
<td align="left">216</td>
<td align="left">35.47</td>
<td align="left">88</td>
<td align="left">33.85</td>
</tr>
<tr class="even">
<td align="left">Queens</td>
<td align="left">449108</td>
<td align="left">20.67</td>
<td align="left">495</td>
<td align="left">19.37</td>
<td align="left">92</td>
<td align="left">15.11</td>
<td align="left">38</td>
<td align="left">14.62</td>
</tr>
<tr class="odd">
<td align="left">Staten Island</td>
<td align="left">57013</td>
<td align="left">2.62</td>
<td align="left">254</td>
<td align="left">9.94</td>
<td align="left">25</td>
<td align="left">4.11</td>
<td align="left">13</td>
<td align="left">5</td>
</tr>
<tr class="even">
<td align="left">Total</td>
<td align="left">2172634</td>
<td align="left">100</td>
<td align="left">2555</td>
<td align="left">100</td>
<td align="left">609</td>
<td align="left">100</td>
<td align="left">260</td>
<td align="left">100</td>
</tr>
</tbody>
</table>
</div>
<div id="figure-a5-p.a-22-map-of-the-geographic-distribution-of-housing-units-corresponding-to-advertised-listings" class="section level2">
<h2><span class="header-section-number">2.8</span> Figure A5 (p. A-22): Map of the Geographic Distribution of Housing Units Corresponding to Advertised Listings</h2>
<p>We omit the replication data and code to produce Figure A5, the map of the geographic distribution of rental units in the audit and experimental samples, because these require non-anonymized data that contain personal identifying information about the subjects.</p>
</div>
<div id="table-a6-p.a-24-baseline-incidence-of-discrimination-in-person-and-post-visit" class="section level2">
<h2><span class="header-section-number">2.9</span> Table A6 (p. A-24): Baseline Incidence of Discrimination: In-Person and Post-Visit</h2>
<pre class="r"><code># One table shell for each comparison (white-black, white-hispanic, black-hispanic)
# 1 = one objective measure, then number of subjective indicaors, then post-visit outcomes, then one for the index measure
wb &lt;- matrix(NA, nrow=1+nrow(net.subj.mat)+nrow(net.post.mat)+1, ncol=16)
wh &lt;- matrix(NA, nrow=1+nrow(net.subj.mat)+nrow(net.post.mat)+1, ncol=16)
bh &lt;- matrix(NA, nrow=1+nrow(net.subj.mat)+nrow(net.post.mat)+1, ncol=16)

# populate column headings
colnames(wb) &lt;- c(&quot;Measure&quot;, paste( c(rep(&quot;Control-&quot;,5),rep(&quot;AnyTreat-&quot;,5),rep(&quot;ExpSamp-&quot;,5)) , rep(c(&quot;Maj-Mean&quot;,&quot;Min-Mean&quot;,&quot;Diff&quot;,&quot;P&quot;,&quot;N&quot;),3), sep=&quot;&quot;))
colnames(wh) &lt;- c(&quot;Measure&quot;, paste( c(rep(&quot;Control-&quot;,5),rep(&quot;AnyTreat-&quot;,5),rep(&quot;ExpSamp-&quot;,5)) , rep(c(&quot;Maj-Mean&quot;,&quot;Min-Mean&quot;,&quot;Diff&quot;,&quot;P&quot;,&quot;N&quot;),3), sep=&quot;&quot;))
colnames(bh) &lt;- c(&quot;Measure&quot;, paste( c(rep(&quot;Control-&quot;,5),rep(&quot;AnyTreat-&quot;,5),rep(&quot;ExpSamp-&quot;,5)) , rep(c(&quot;Maj-Mean&quot;,&quot;Min-Mean&quot;,&quot;Diff&quot;,&quot;P&quot;,&quot;N&quot;),3), sep=&quot;&quot;))

# populate first column - measure names
wb[,1] &lt;- c(net.obj.mat.labs[1,1], net.subj.mat.labs[,1], net.post.mat.labs[,1],net.ind.mat.labs[1])
wh[,1] &lt;- c(net.obj.mat.labs[1,2], net.subj.mat.labs[,2], net.post.mat.labs[,2],net.ind.mat.labs[2])
bh[,1] &lt;- c(net.obj.mat.labs[1,3], net.subj.mat.labs[,3], net.post.mat.labs[,3],net.ind.mat.labs[3])

# check that all vectors are the same length
# length(outvars.a)==length(outvars.b)
# length(outvars.a)==length(outvars.c)
# length(outvars.a)==length(outvars.wb)
# length(outvars.a)==length(outvars.wh)
# length(outvars.a)==length(outvars.bh)

# fill in shell for wb comparison
for(i in 1:length(outvars.a)){
  ## CONTROL GROUP (cols 2-6)
  wb[i,2] &lt;- mean(as.numeric(dat[[outvars.c[i]]][dat$TA==0]), na.rm=TRUE) # majority mean
  wb[i,3] &lt;- mean(as.numeric(dat[[outvars.a[i]]][dat$TA==0]), na.rm=TRUE) # minority mean
  wb[i,4] &lt;- as.numeric(wb[i,2]) - as.numeric(wb[i,3]) # difference
  #  print(as.numeric(wb[i,2]) - as.numeric(wb[i,3]))
  #  print(mean(as.numeric(dat[[outvars.wb[i]]][dat$insamp==1 &amp; !is.na(dat$insamp) &amp; dat$TA==0]), na.rm=TRUE))
  # PAIRED T-TESTS FOR NET MEASURES WITH NO MISSING DATA, NON-PAIRED OTHERWISE -- CONTROL STRUCTURE CONDITIONS ON VARIABLE
  if(i %in% c(1,7,8)){
    wb[i,5] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA==0]),
                      y=as.numeric(dat[[outvars.a[i]]][dat$TA==0]),
                      alternative=&quot;two.sided&quot;,
                      paired=TRUE,
                      conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    wb[i,5] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA==0]),
                      y=as.numeric(dat[[outvars.a[i]]][dat$TA==0]),
                      alternative=&quot;two.sided&quot;,
                      paired=FALSE,
                      conf.level=0.95)$p.value # p value from t-test (two-sided)    
  }
  
  
  wb[i,6] &lt;- ifelse(length(dat[[outvars.c[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.c[i]]])])==length(as.numeric(dat[[outvars.a[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.a[i]]])])),
                    length(dat[[outvars.c[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.c[i]]])]),
                    length(dat[[outvars.c[i]]][dat$TA==0 &amp; !( is.na(dat[[outvars.c[i]]]) &amp; is.na(dat[[outvars.a[i]]]) )])  )# sample size
  
  ## ANY TREATMENT (cols 7-11)
  wb[i,7] &lt;- mean(as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(1,2)]), na.rm=TRUE)  # majority mean
  wb[i,8] &lt;- mean(as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(1,2)]), na.rm=TRUE)  # minority mean
  wb[i,9] &lt;- as.numeric(wb[i,7]) - as.numeric(wb[i,8])  # difference
  if(i %in% c(1,7,8)){
    wb[i,10] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(1,2)]),
                       y=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=TRUE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    wb[i,10] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(1,2)]),
                       y=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=FALSE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)    
  }
  wb[i,11] &lt;- ifelse(length(dat[[outvars.c[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.c[i]]])])==length(as.numeric(dat[[outvars.a[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.a[i]]])])),
                     length(dat[[outvars.c[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.c[i]]])]),
                     length(dat[[outvars.c[i]]][dat$TA%in%c(1,2) &amp; !( is.na(dat[[outvars.c[i]]]) &amp; is.na(dat[[outvars.a[i]]]) )]) )# sample size
  
  ## EXPERIMENT SAMPLE (cols 12-16)
  wb[i,12] &lt;- mean(as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(0,1,2)]), na.rm=TRUE)  # majority mean
  wb[i,13] &lt;- mean(as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(0,1,2)]), na.rm=TRUE) # minority mean
  wb[i,14] &lt;- as.numeric(wb[i,12]) - as.numeric(wb[i,13])  # difference
  if(i %in% c(1,7,8)){
    wb[i,15] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(0,1,2)]),
                       y=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(0,1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=TRUE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    wb[i,15] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(0,1,2)]),
                       y=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(0,1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=FALSE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)    
  }
  wb[i,16] &lt;-  ifelse(length(dat[[outvars.c[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.c[i]]])])==length(as.numeric(dat[[outvars.a[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.a[i]]])])),
                      length(dat[[outvars.c[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.c[i]]])]),
                      length(dat[[outvars.c[i]]][dat$TA%in%c(0,1,2) &amp; !( is.na(dat[[outvars.c[i]]]) &amp; is.na(dat[[outvars.a[i]]]) )])  )# sample size
}
## CONTROL GROUP
wb[nrow(wb),4] &lt;- mean(as.numeric(dat[[net.ind.mat[1]]][dat$TA==0]), na.rm=TRUE) # difference
wb[nrow(wb),6] &lt;- length(as.numeric(dat[[net.ind.mat[1]]][dat$TA%in%c(0) &amp; !is.na(dat[[net.ind.mat[1]]])])) # sample size
## ANY TREATMENT
wb[nrow(wb),9] &lt;- mean(as.numeric(dat[[net.ind.mat[1]]][dat$TA%in%c(1,2)]), na.rm=TRUE) # difference
wb[nrow(wb),11] &lt;- length(as.numeric(dat[[net.ind.mat[1]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[net.ind.mat[1]]])])) # sample size
## EXPERIMENT SAMPLE
wb[nrow(wb),14] &lt;- mean(as.numeric(dat[[net.ind.mat[1]]][dat$TA%in%c(0,1,2)]), na.rm=TRUE) # difference
wb[nrow(wb),16] &lt;- length(as.numeric(dat[[net.ind.mat[1]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[net.ind.mat[1]]])]))  # sample size     

# fill in shell for wh comparison
for(i in 1:length(outvars.b)){
  ## CONTROL GROUP (cols 2-6)
  wh[i,2] &lt;- mean(as.numeric(dat[[outvars.c[i]]][dat$TA==0]), na.rm=TRUE) # majority mean
  wh[i,3] &lt;- mean(as.numeric(dat[[outvars.b[i]]][dat$TA==0]), na.rm=TRUE) # minority mean
  wh[i,4] &lt;- as.numeric(wh[i,2]) - as.numeric(wh[i,3]) # difference
  #  print(as.numeric(wh[i,2]) - as.numeric(wh[i,3]))
  #  print(mean(as.numeric(dat[[outvars.wh[i]]][dat$insamp==1 &amp; !is.na(dat$insamp) &amp; dat$TA==0]), na.rm=TRUE))
  if(i %in% c(1,7,8)){
    wh[i,5] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA==0]),
                      y=as.numeric(dat[[outvars.b[i]]][dat$TA==0]),
                      alternative=&quot;two.sided&quot;,
                      paired=TRUE,
                      conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    wh[i,5] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA==0]),
                      y=as.numeric(dat[[outvars.b[i]]][dat$TA==0]),
                      alternative=&quot;two.sided&quot;,
                      paired=FALSE,
                      conf.level=0.95)$p.value # p value from t-test (two-sided)
    
  }
  wh[i,6] &lt;- ifelse(length(dat[[outvars.c[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.c[i]]])])==length(as.numeric(dat[[outvars.b[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.b[i]]])])),
                    length(dat[[outvars.c[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.c[i]]])]),
                    length(dat[[outvars.c[i]]][dat$TA%in%c(0) &amp; !( is.na(dat[[outvars.c[i]]]) &amp; is.na(dat[[outvars.b[i]]]) )]) )# sample size
  
  ## ANY TREATMENT (cols 7-11)
  wh[i,7] &lt;- mean(as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(1,2)]), na.rm=TRUE)  # majority mean
  wh[i,8] &lt;- mean(as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(1,2)]), na.rm=TRUE)  # minority mean
  wh[i,9] &lt;- as.numeric(wh[i,7]) - as.numeric(wh[i,8])  # difference
  if(i %in% c(1,7,8)){
    wh[i,10] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(1,2)]),
                       y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=TRUE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    wh[i,10] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(1,2)]),
                       y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=FALSE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)    
  }
  wh[i,11] &lt;- ifelse(length(dat[[outvars.c[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.c[i]]])])==length(as.numeric(dat[[outvars.b[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.b[i]]])])),
                     length(dat[[outvars.c[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.c[i]]])]),
                     length(dat[[outvars.c[i]]][dat$TA%in%c(1,2) &amp; !( is.na(dat[[outvars.c[i]]]) &amp; is.na(dat[[outvars.b[i]]]) )])  )# sample size
  
  ## EXPERIMENT SAMPLE (cols 12-16)
  wh[i,12] &lt;- mean(as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(0,1,2)]), na.rm=TRUE)  # majority mean
  wh[i,13] &lt;- mean(as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(0,1,2)]), na.rm=TRUE) # minority mean
  wh[i,14] &lt;- as.numeric(wh[i,12]) - as.numeric(wh[i,13])  # difference
  wh[i,15] &lt;- t.test(x=as.numeric(dat[[outvars.c[i]]][dat$TA %in% c(0,1,2)]),
                     y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(0,1,2)]),
                     alternative=&quot;two.sided&quot;,
                     conf.level=0.95)$p.value # p value from t-test (two-sided)
  wh[i,16] &lt;-  ifelse(length(dat[[outvars.c[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.c[i]]])])==length(as.numeric(dat[[outvars.b[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.b[i]]])])),
                      length(dat[[outvars.c[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.c[i]]])]),
                      length(dat[[outvars.c[i]]][dat$TA%in%c(0,1,2) &amp; !( is.na(dat[[outvars.c[i]]]) &amp; is.na(dat[[outvars.b[i]]]) )]) )# sample size
}
## CONTROL GROUP
wh[nrow(wh),4] &lt;- mean(as.numeric(dat[[net.ind.mat[2]]][dat$TA==0]), na.rm=TRUE) # difference
wh[nrow(wh),6] &lt;- length(as.numeric(dat[[net.ind.mat[2]]][dat$TA%in%c(0) &amp; !is.na(dat[[net.ind.mat[2]]])])) # sample size
## ANY TREATMENT
wh[nrow(wh),9] &lt;- mean(as.numeric(dat[[net.ind.mat[2]]][dat$TA%in%c(1,2)]), na.rm=TRUE) # difference
wh[nrow(wh),11] &lt;- length(as.numeric(dat[[net.ind.mat[2]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[net.ind.mat[2]]])])) # sample size
## EXPERIMENT SAMPLE
wh[nrow(wh),14] &lt;- mean(as.numeric(dat[[net.ind.mat[2]]][dat$TA%in%c(0,1,2)]), na.rm=TRUE) # difference
wh[nrow(wh),16] &lt;- length(as.numeric(dat[[net.ind.mat[2]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[net.ind.mat[2]]])]))  # sample size     

# fill in shell for bh comparison
for(i in 1:length(outvars.b)){
  ## CONTROL GROUP (cols 2-6)
  bh[i,2] &lt;- mean(as.numeric(dat[[outvars.a[i]]][dat$TA==0]), na.rm=TRUE) # majority mean
  bh[i,3] &lt;- mean(as.numeric(dat[[outvars.b[i]]][dat$TA==0]), na.rm=TRUE) # minority mean
  bh[i,4] &lt;- as.numeric(bh[i,2]) - as.numeric(bh[i,3]) # difference
  #  print(as.numeric(bh[i,2]) - as.numeric(bh[i,3]))
  #  print(mean(as.numeric(dat[[outvars.bh[i]]][dat$insamp==1 &amp; !is.na(dat$insamp) &amp; dat$TA==0]), na.rm=TRUE))
  if(i %in% c(1,7,8)){
    bh[i,5] &lt;- t.test(x=as.numeric(dat[[outvars.a[i]]][dat$TA==0]),
                      y=as.numeric(dat[[outvars.b[i]]][dat$TA==0]),
                      alternative=&quot;two.sided&quot;,
                      paired=TRUE,
                      conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    bh[i,5] &lt;- t.test(x=as.numeric(dat[[outvars.a[i]]][dat$TA==0]),
                      y=as.numeric(dat[[outvars.b[i]]][dat$TA==0]),
                      alternative=&quot;two.sided&quot;,
                      paired=FALSE,
                      conf.level=0.95)$p.value # p value from t-test (two-sided)    
  }
  bh[i,6] &lt;- ifelse(length(dat[[outvars.a[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.a[i]]])])==length(as.numeric(dat[[outvars.b[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.b[i]]])])),
                    length(dat[[outvars.a[i]]][dat$TA==0 &amp; !is.na(dat[[outvars.a[i]]])]),
                    length(dat[[outvars.a[i]]][dat$TA%in%c(0) &amp; !( is.na(dat[[outvars.a[i]]]) &amp; is.na(dat[[outvars.b[i]]]) )])  )# sample size
  
  ## ANY TREATMENT (cols 7-11)
  bh[i,7] &lt;- mean(as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(1,2)]), na.rm=TRUE)  # majority mean
  bh[i,8] &lt;- mean(as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(1,2)]), na.rm=TRUE)  # minority mean
  bh[i,9] &lt;- as.numeric(bh[i,7]) - as.numeric(bh[i,8])  # difference
  if(i %in% c(1,7,8)){
    bh[i,10] &lt;- t.test(x=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(1,2)]),
                       y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=TRUE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    bh[i,10] &lt;- t.test(x=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(1,2)]),
                       y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=FALSE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)
  }
  bh[i,11] &lt;- ifelse(length(dat[[outvars.a[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.a[i]]])])==length(as.numeric(dat[[outvars.b[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.b[i]]])])),
                     length(dat[[outvars.a[i]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[outvars.a[i]]])]),
                     length(dat[[outvars.a[i]]][dat$TA%in%c(1,2) &amp; !( is.na(dat[[outvars.a[i]]]) &amp; is.na(dat[[outvars.b[i]]]) )])  )# sample size
  
  ## EXPERIMENT SAMPLE (cols 12-16)
  bh[i,12] &lt;- mean(as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(0,1,2)]), na.rm=TRUE)  # majority mean
  bh[i,13] &lt;- mean(as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(0,1,2)]), na.rm=TRUE) # minority mean
  bh[i,14] &lt;- as.numeric(bh[i,12]) - as.numeric(bh[i,13])  # difference
  if(i %in% c(1,7,8)){
    bh[i,15] &lt;- t.test(x=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(0,1,2)]),
                       y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(0,1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=TRUE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)
  } else {
    bh[i,15] &lt;- t.test(x=as.numeric(dat[[outvars.a[i]]][dat$TA %in% c(0,1,2)]),
                       y=as.numeric(dat[[outvars.b[i]]][dat$TA %in% c(0,1,2)]),
                       alternative=&quot;two.sided&quot;,
                       paired=FALSE,
                       conf.level=0.95)$p.value # p value from t-test (two-sided)    
  }
  bh[i,16] &lt;-  ifelse(length(dat[[outvars.a[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.a[i]]])])==length(as.numeric(dat[[outvars.b[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.b[i]]])])),
                      length(dat[[outvars.a[i]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[outvars.a[i]]])]),
                      length(dat[[outvars.a[i]]][dat$TA%in%c(0,1,2) &amp; !( is.na(dat[[outvars.a[i]]]) &amp; is.na(dat[[outvars.b[i]]]) )])  )# sample size
}
## CONTROL GROUP
bh[nrow(bh),4] &lt;- mean(as.numeric(dat[[net.ind.mat[3]]][dat$TA==0]), na.rm=TRUE) # difference
bh[nrow(bh),6] &lt;- length(as.numeric(dat[[net.ind.mat[3]]][dat$TA%in%c(0) &amp; !is.na(dat[[net.ind.mat[3]]])])) # sample size
## ANY TREATMENT
bh[nrow(bh),9] &lt;- mean(as.numeric(dat[[net.ind.mat[3]]][dat$TA%in%c(1,2)]), na.rm=TRUE) # difference
bh[nrow(bh),11] &lt;- length(as.numeric(dat[[net.ind.mat[3]]][dat$TA%in%c(1,2) &amp; !is.na(dat[[net.ind.mat[3]]])])) # sample size
## EXPERIMENT SAMPLE
bh[nrow(bh),14] &lt;- mean(as.numeric(dat[[net.ind.mat[3]]][dat$TA%in%c(0,1,2)]), na.rm=TRUE) # difference
bh[nrow(bh),16] &lt;- length(as.numeric(dat[[net.ind.mat[3]]][dat$TA%in%c(0,1,2) &amp; !is.na(dat[[net.ind.mat[3]]])]))  # sample size     


for(i in 2:ncol(wb)){
  wb[,i] &lt;- round(as.numeric(wb[,i]), 3)
  wh[,i] &lt;- round(as.numeric(wh[,i]), 3)
  bh[,i] &lt;- round(as.numeric(bh[,i]), 3)
  
  if(i %in% c(5,10,15)) {
    wb[,i] &lt;- paste(&quot;(&quot;,wb[,i],&quot;)&quot;,sep=&quot;&quot;)
    wh[,i] &lt;- paste(&quot;(&quot;,wh[,i],&quot;)&quot;,sep=&quot;&quot;)
    bh[,i] &lt;- paste(&quot;(&quot;,bh[,i],&quot;)&quot;,sep=&quot;&quot;)
  }
  if(i %in% c(6,11,16)) {
    wb[,i] &lt;- paste(&quot;[&quot;,wb[,i],&quot;]&quot;,sep=&quot;&quot;)
    wh[,i] &lt;- paste(&quot;[&quot;,wh[,i],&quot;]&quot;,sep=&quot;&quot;)
    bh[,i] &lt;- paste(&quot;[&quot;,bh[,i],&quot;]&quot;,sep=&quot;&quot;)
  }
}



out &lt;- rbind(wb[c(1,9,2:8),],
             wh[c(1,9,2:8),],
             bh[c(1,9,2:8),])

out &lt;- out[-c(2,11,20),] # get rid of index measure (it makes no sense to include it because it is standardized to the control group mean)

print(xtable(out, caption=c(&quot;Baseline Incidence of Discrimination: In-Person and Post-Visit&quot;,
                            &quot;Baseline Incidence of Discrimination: In-Person and Post-Visit&quot;)), 
      file=&quot;out_tablea6_baseline_discrim.tex&quot;,
      hline.after=c(0,8,16,24), 
      include.rownames=FALSE)

kable(out[,c(1:6)], caption=&quot;**Table A6, Panel I: Cases Assigned to Control Group**&quot;, col.names=c(&quot;Measure&quot;, &quot;Majority Group Mean&quot;, &quot;Minority Group Mean&quot;, &quot;Difference (Maj-Min)&quot;, &quot;p-value&quot;, &quot;[N]&quot;))</code></pre>
<table>
<caption><strong>Table A6, Panel I: Cases Assigned to Control Group</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Showed up to appointment (White vs. Black)</td>
<td align="left">0.824</td>
<td align="left">0.839</td>
<td align="left">-0.014</td>
<td align="left">(0.538)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (White vs. Black)</td>
<td align="left">0.417</td>
<td align="left">0.509</td>
<td align="left">-0.091</td>
<td align="left">(0.049)</td>
<td align="left">[253]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (White vs. Black)</td>
<td align="left">0.061</td>
<td align="left">0.068</td>
<td align="left">-0.008</td>
<td align="left">(0.743)</td>
<td align="left">[253]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (White vs. Black)</td>
<td align="left">0.017</td>
<td align="left">0.004</td>
<td align="left">0.013</td>
<td align="left">(0.174)</td>
<td align="left">[253]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (White vs. Black)</td>
<td align="left">0.817</td>
<td align="left">0.765</td>
<td align="left">0.052</td>
<td align="left">(0.165)</td>
<td align="left">[253]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (White vs. Black)</td>
<td align="left">0.522</td>
<td align="left">0.483</td>
<td align="left">0.039</td>
<td align="left">(0.404)</td>
<td align="left">[253]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.215</td>
<td align="left">0.168</td>
<td align="left">0.047</td>
<td align="left">(0.107)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.118</td>
<td align="left">0.09</td>
<td align="left">0.029</td>
<td align="left">(0.239)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Showed up to appointment (White vs. Hispanic)</td>
<td align="left">0.824</td>
<td align="left">0.789</td>
<td align="left">0.036</td>
<td align="left">(0.174)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (White vs. Hispanic)</td>
<td align="left">0.417</td>
<td align="left">0.45</td>
<td align="left">-0.033</td>
<td align="left">(0.486)</td>
<td align="left">[252]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (White vs. Hispanic)</td>
<td align="left">0.061</td>
<td align="left">0.045</td>
<td align="left">0.015</td>
<td align="left">(0.467)</td>
<td align="left">[252]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (White vs. Hispanic)</td>
<td align="left">0.017</td>
<td align="left">0.009</td>
<td align="left">0.008</td>
<td align="left">(0.441)</td>
<td align="left">[252]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (White vs. Hispanic)</td>
<td align="left">0.817</td>
<td align="left">0.786</td>
<td align="left">0.031</td>
<td align="left">(0.411)</td>
<td align="left">[252]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (White vs. Hispanic)</td>
<td align="left">0.522</td>
<td align="left">0.591</td>
<td align="left">-0.069</td>
<td align="left">(0.14)</td>
<td align="left">[252]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">0.215</td>
<td align="left">0.154</td>
<td align="left">0.061</td>
<td align="left">(0.019)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.118</td>
<td align="left">0.061</td>
<td align="left">0.057</td>
<td align="left">(0.011)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Showed up to appointment (Black vs. Hispanic)</td>
<td align="left">0.839</td>
<td align="left">0.789</td>
<td align="left">0.05</td>
<td align="left">(0.043)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (Black vs. Hispanic)</td>
<td align="left">0.509</td>
<td align="left">0.45</td>
<td align="left">0.059</td>
<td align="left">(0.213)</td>
<td align="left">[251]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (Black vs. Hispanic)</td>
<td align="left">0.068</td>
<td align="left">0.045</td>
<td align="left">0.023</td>
<td align="left">(0.292)</td>
<td align="left">[251]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (Black vs. Hispanic)</td>
<td align="left">0.004</td>
<td align="left">0.009</td>
<td align="left">-0.005</td>
<td align="left">(0.532)</td>
<td align="left">[251]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (Black vs. Hispanic)</td>
<td align="left">0.765</td>
<td align="left">0.786</td>
<td align="left">-0.021</td>
<td align="left">(0.586)</td>
<td align="left">[251]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (Black vs. Hispanic)</td>
<td align="left">0.483</td>
<td align="left">0.591</td>
<td align="left">-0.108</td>
<td align="left">(0.021)</td>
<td align="left">[251]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">0.168</td>
<td align="left">0.154</td>
<td align="left">0.014</td>
<td align="left">(0.587)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">0.09</td>
<td align="left">0.061</td>
<td align="left">0.029</td>
<td align="left">(0.17)</td>
<td align="left">[279]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(out[,c(1, 7:11)], caption=&quot;**Table A6, Panel II: Cases Assigned to Any Treatment Group**&quot;, col.names=c(&quot;Measure&quot;, &quot;Majority Group Mean&quot;, &quot;Minority Group Mean&quot;, &quot;Difference (Maj-Min)&quot;, &quot;p-value&quot;, &quot;[N]&quot;))</code></pre>
<table>
<caption><strong>Table A6, Panel II: Cases Assigned to Any Treatment Group</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Showed up to appointment (White vs. Black)</td>
<td align="left">0.818</td>
<td align="left">0.781</td>
<td align="left">0.037</td>
<td align="left">(0.094)</td>
<td align="left">[374]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (White vs. Black)</td>
<td align="left">0.436</td>
<td align="left">0.421</td>
<td align="left">0.015</td>
<td align="left">(0.715)</td>
<td align="left">[334]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (White vs. Black)</td>
<td align="left">0.052</td>
<td align="left">0.045</td>
<td align="left">0.008</td>
<td align="left">(0.652)</td>
<td align="left">[334]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (White vs. Black)</td>
<td align="left">0.007</td>
<td align="left">0.003</td>
<td align="left">0.003</td>
<td align="left">(0.587)</td>
<td align="left">[334]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (White vs. Black)</td>
<td align="left">0.797</td>
<td align="left">0.743</td>
<td align="left">0.054</td>
<td align="left">(0.121)</td>
<td align="left">[334]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (White vs. Black)</td>
<td align="left">0.498</td>
<td align="left">0.507</td>
<td align="left">-0.008</td>
<td align="left">(0.836)</td>
<td align="left">[334]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.187</td>
<td align="left">0.131</td>
<td align="left">0.056</td>
<td align="left">(0.018)</td>
<td align="left">[374]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.094</td>
<td align="left">0.08</td>
<td align="left">0.013</td>
<td align="left">(0.467)</td>
<td align="left">[374]</td>
</tr>
<tr class="odd">
<td align="left">Showed up to appointment (White vs. Hispanic)</td>
<td align="left">0.818</td>
<td align="left">0.794</td>
<td align="left">0.024</td>
<td align="left">(0.272)</td>
<td align="left">[374]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (White vs. Hispanic)</td>
<td align="left">0.436</td>
<td align="left">0.394</td>
<td align="left">0.042</td>
<td align="left">(0.295)</td>
<td align="left">[334]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (White vs. Hispanic)</td>
<td align="left">0.052</td>
<td align="left">0.081</td>
<td align="left">-0.028</td>
<td align="left">(0.164)</td>
<td align="left">[334]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (White vs. Hispanic)</td>
<td align="left">0.007</td>
<td align="left">0.007</td>
<td align="left">0</td>
<td align="left">(0.979)</td>
<td align="left">[334]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (White vs. Hispanic)</td>
<td align="left">0.797</td>
<td align="left">0.838</td>
<td align="left">-0.042</td>
<td align="left">(0.186)</td>
<td align="left">[334]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (White vs. Hispanic)</td>
<td align="left">0.498</td>
<td align="left">0.458</td>
<td align="left">0.04</td>
<td align="left">(0.321)</td>
<td align="left">[334]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">0.187</td>
<td align="left">0.171</td>
<td align="left">0.016</td>
<td align="left">(0.503)</td>
<td align="left">[374]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.094</td>
<td align="left">0.08</td>
<td align="left">0.013</td>
<td align="left">(0.476)</td>
<td align="left">[374]</td>
</tr>
<tr class="odd">
<td align="left">Showed up to appointment (Black vs. Hispanic)</td>
<td align="left">0.781</td>
<td align="left">0.794</td>
<td align="left">-0.013</td>
<td align="left">(0.597)</td>
<td align="left">[374]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (Black vs. Hispanic)</td>
<td align="left">0.421</td>
<td align="left">0.394</td>
<td align="left">0.027</td>
<td align="left">(0.501)</td>
<td align="left">[339]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (Black vs. Hispanic)</td>
<td align="left">0.045</td>
<td align="left">0.081</td>
<td align="left">-0.036</td>
<td align="left">(0.069)</td>
<td align="left">[339]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (Black vs. Hispanic)</td>
<td align="left">0.003</td>
<td align="left">0.007</td>
<td align="left">-0.003</td>
<td align="left">(0.572)</td>
<td align="left">[339]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (Black vs. Hispanic)</td>
<td align="left">0.743</td>
<td align="left">0.838</td>
<td align="left">-0.095</td>
<td align="left">(0.004)</td>
<td align="left">[339]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (Black vs. Hispanic)</td>
<td align="left">0.507</td>
<td align="left">0.458</td>
<td align="left">0.049</td>
<td align="left">(0.235)</td>
<td align="left">[339]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">0.131</td>
<td align="left">0.171</td>
<td align="left">-0.04</td>
<td align="left">(0.079)</td>
<td align="left">[374]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">0.08</td>
<td align="left">0.08</td>
<td align="left">0</td>
<td align="left">(1)</td>
<td align="left">[374]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(out[,c(1, 12:16)], caption=&quot;**Table A6, Panel III: All Cases in Experimental Sample**&quot;, col.names=c(&quot;Measure&quot;, &quot;Majority Group Mean&quot;, &quot;Minority Group Mean&quot;, &quot;Difference (Maj-Min)&quot;, &quot;p-value&quot;, &quot;[N]&quot;))</code></pre>
<table>
<caption><strong>Table A6, Panel III: All Cases in Experimental Sample</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Showed up to appointment (White vs. Black)</td>
<td align="left">0.821</td>
<td align="left">0.806</td>
<td align="left">0.015</td>
<td align="left">(0.345)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (White vs. Black)</td>
<td align="left">0.428</td>
<td align="left">0.46</td>
<td align="left">-0.032</td>
<td align="left">(0.294)</td>
<td align="left">[587]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (White vs. Black)</td>
<td align="left">0.056</td>
<td align="left">0.055</td>
<td align="left">0.001</td>
<td align="left">(0.947)</td>
<td align="left">[587]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (White vs. Black)</td>
<td align="left">0.011</td>
<td align="left">0.004</td>
<td align="left">0.007</td>
<td align="left">(0.161)</td>
<td align="left">[587]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (White vs. Black)</td>
<td align="left">0.806</td>
<td align="left">0.753</td>
<td align="left">0.053</td>
<td align="left">(0.038)</td>
<td align="left">[587]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (White vs. Black)</td>
<td align="left">0.508</td>
<td align="left">0.496</td>
<td align="left">0.012</td>
<td align="left">(0.691)</td>
<td align="left">[587]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.199</td>
<td align="left">0.147</td>
<td align="left">0.052</td>
<td align="left">(0.005)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.104</td>
<td align="left">0.084</td>
<td align="left">0.02</td>
<td align="left">(0.178)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Showed up to appointment (White vs. Hispanic)</td>
<td align="left">0.821</td>
<td align="left">0.792</td>
<td align="left">0.029</td>
<td align="left">(0.184)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (White vs. Hispanic)</td>
<td align="left">0.428</td>
<td align="left">0.418</td>
<td align="left">0.01</td>
<td align="left">(0.737)</td>
<td align="left">[586]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (White vs. Hispanic)</td>
<td align="left">0.056</td>
<td align="left">0.066</td>
<td align="left">-0.01</td>
<td align="left">(0.512)</td>
<td align="left">[586]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (White vs. Hispanic)</td>
<td align="left">0.011</td>
<td align="left">0.008</td>
<td align="left">0.003</td>
<td align="left">(0.56)</td>
<td align="left">[586]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (White vs. Hispanic)</td>
<td align="left">0.806</td>
<td align="left">0.816</td>
<td align="left">-0.011</td>
<td align="left">(0.66)</td>
<td align="left">[586]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (White vs. Hispanic)</td>
<td align="left">0.508</td>
<td align="left">0.515</td>
<td align="left">-0.006</td>
<td align="left">(0.843)</td>
<td align="left">[586]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">0.199</td>
<td align="left">0.164</td>
<td align="left">0.035</td>
<td align="left">(0.099)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.104</td>
<td align="left">0.072</td>
<td align="left">0.032</td>
<td align="left">(0.04)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Showed up to appointment (Black vs. Hispanic)</td>
<td align="left">0.806</td>
<td align="left">0.792</td>
<td align="left">0.014</td>
<td align="left">(0.442)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Perceived sales efforts (Black vs. Hispanic)</td>
<td align="left">0.46</td>
<td align="left">0.418</td>
<td align="left">0.042</td>
<td align="left">(0.169)</td>
<td align="left">[590]</td>
</tr>
<tr class="odd">
<td align="left">Received praise about rental qualifications (Black vs. Hispanic)</td>
<td align="left">0.055</td>
<td align="left">0.066</td>
<td align="left">-0.011</td>
<td align="left">(0.472)</td>
<td align="left">[590]</td>
</tr>
<tr class="even">
<td align="left">Positive reactions to testers’ background (Black vs. Hispanic)</td>
<td align="left">0.004</td>
<td align="left">0.008</td>
<td align="left">-0.004</td>
<td align="left">(0.403)</td>
<td align="left">[590]</td>
</tr>
<tr class="odd">
<td align="left">Positive editorializing (Black vs. Hispanic)</td>
<td align="left">0.753</td>
<td align="left">0.816</td>
<td align="left">-0.063</td>
<td align="left">(0.013)</td>
<td align="left">[590]</td>
</tr>
<tr class="even">
<td align="left">Professionalism (Black vs. Hispanic)</td>
<td align="left">0.496</td>
<td align="left">0.515</td>
<td align="left">-0.018</td>
<td align="left">(0.555)</td>
<td align="left">[590]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">0.147</td>
<td align="left">0.164</td>
<td align="left">-0.017</td>
<td align="left">(0.329)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">0.084</td>
<td align="left">0.072</td>
<td align="left">0.012</td>
<td align="left">(0.359)</td>
<td align="left">[653]</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a7-p.a-25-estimated-effects-of-messaging-on-net-discrimination-levels" class="section level2">
<h2><span class="header-section-number">2.10</span> Table A7 (p. A-25): Estimated Effects of Messaging on Net Discrimination Levels</h2>
<pre class="r"><code># ---------------------------------------------------------------------- #
# DEFINE COLNAMES FOR OUTPUT TABLES

col.labels &lt;- c(&quot;Outcome&quot;,c(&quot;Estimate&quot;,&quot;SE&quot;,&quot;t&quot;,&quot;P&quot;))

# ---------------------------------------------------------------------- #
# MODELS WITH ONLY BLOCK FIXED EFFECTS

itt.wb.mc &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)
itt.wb.pc &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)
itt.wb.pm &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)

itt.wh.mc &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)
itt.wh.pc &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)
itt.wh.pm &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)

itt.bh.mc &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)
itt.bh.pc &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)
itt.bh.pm &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)

fit.wb.mc &lt;- fit.wb.pc &lt;- fit.wb.pm &lt;- fit.wh.mc &lt;- fit.wh.pc &lt;- fit.wh.pm &lt;- fit.bh.mc &lt;- fit.bh.pc &lt;- fit.bh.pm &lt;- list() # store results from ITT est w/ block FE (no other covs)

for(i in 1:length(outvars.wb)){  
  model.mc &lt;- paste(outvars.wb[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)

  fit.wb.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.wb.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.wb.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)
  
  itt.wb.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.wb.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.wb.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
    
  itt.wb.mc[i,5] &lt;- pt(coef(summary(fit.mc))[,3], summary(fit.mc)$df[2], lower=TRUE)[2] #one sided p
  itt.wb.pc[i,5] &lt;- pt(coef(summary(fit.pc))[,3], summary(fit.pc)$df[2], lower=TRUE)[2] #one sided p
}

for(i in 1:length(outvars.wh)){  
  model.mc &lt;- paste(outvars.wh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.wh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.wh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.wh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)
  
  itt.wh.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.wh.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.wh.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
  
  itt.wh.mc[i,5] &lt;- pt(coef(summary(fit.mc))[,3], summary(fit.mc)$df[2], lower=TRUE)[2] #one sided p
  itt.wh.pc[i,5] &lt;- pt(coef(summary(fit.pc))[,3], summary(fit.pc)$df[2], lower=TRUE)[2] #one sided p
}

for(i in 1:length(outvars.bh)){  
  model.mc &lt;- paste(outvars.bh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.bh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.bh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.bh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)
  
  itt.bh.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.bh.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.bh.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
}

# Add outcome measure labels
itt.wb.mc[,1] &lt;- outvars.wb.labs
itt.wb.pc[,1] &lt;- outvars.wb.labs
itt.wb.pm[,1] &lt;- outvars.wb.labs

itt.wh.mc[,1] &lt;- outvars.wh.labs
itt.wh.pc[,1] &lt;- outvars.wh.labs
itt.wh.pm[,1] &lt;- outvars.wh.labs

itt.bh.mc[,1] &lt;- outvars.bh.labs
itt.bh.pc[,1] &lt;- outvars.bh.labs
itt.bh.pm[,1] &lt;- outvars.bh.labs

# Add column names
colnames(itt.wb.mc) &lt;- colnames(itt.wb.pc) &lt;- colnames(itt.wb.pm) &lt;- col.labels
colnames(itt.wh.mc) &lt;- colnames(itt.wh.pc) &lt;- colnames(itt.wh.pm) &lt;- col.labels
colnames(itt.bh.mc) &lt;- colnames(itt.bh.pc) &lt;- colnames(itt.bh.pm) &lt;- col.labels

# Stack, create output table
out2 &lt;- rbind(itt.wb.mc, itt.wh.mc, itt.bh.mc,
              itt.wb.pc, itt.wh.pc, itt.bh.pc,
              itt.wb.pm, itt.wh.pm, itt.bh.pm)

out2 &lt;- out2[-seq(1,33,4),]

#----- CIs -----#

df.wb.mc &lt;- unlist(lapply(fit.wb.mc, function(x) summary(x)$df[2]))[2:4]
df.wb.pc &lt;- unlist(lapply(fit.wb.pc, function(x) summary(x)$df[2]))[2:4]
df.wb.pm &lt;- unlist(lapply(fit.wb.pm, function(x) summary(x)$df[2]))[2:4]

df.wh.mc &lt;- unlist(lapply(fit.wh.mc, function(x) summary(x)$df[2]))[2:4]
df.wh.pc &lt;- unlist(lapply(fit.wh.pc, function(x) summary(x)$df[2]))[2:4]
df.wh.pm &lt;- unlist(lapply(fit.wh.pm, function(x) summary(x)$df[2]))[2:4]

df.bh.mc &lt;- unlist(lapply(fit.bh.mc, function(x) summary(x)$df[2]))[2:4]
df.bh.pc &lt;- unlist(lapply(fit.bh.pc, function(x) summary(x)$df[2]))[2:4]
df.bh.pm &lt;- unlist(lapply(fit.bh.pm, function(x) summary(x)$df[2]))[2:4]

df.2g.10 &lt;- c(df.wb.mc, df.wh.mc, df.bh.mc)
df.2g.20 &lt;- c(df.wb.pc, df.wh.pc, df.bh.pc)
df.2g.21 &lt;- c(df.wb.pm, df.wh.pm, df.bh.pm)

cv.2g.10 &lt;- qt(p=0.025, df=df.2g.10, lower.tail=TRUE)
cv.2g.20 &lt;- qt(p=0.025, df=df.2g.20, lower.tail=TRUE)
cv.2g.21 &lt;- qt(p=0.025, df=df.2g.21, lower.tail=TRUE)

cv.2g &lt;- c(cv.2g.10, cv.2g.20, cv.2g.21)
itt.2g.ci &lt;- as.numeric(out2[,2]) + abs(cv.2g)*cbind(-as.numeric(out2[,3]), as.numeric(out2[,3]))

# without rounding
#cbind(apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, x[1] ,&quot;, &quot;, x[2], &quot;]&quot;, sep=&quot;&quot;)))

# with rounding
#cbind(apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, round(x[1],3) ,&quot;, &quot;, round(x[2],3), &quot;]&quot;, sep=&quot;&quot;)))

# Round and format results
for(i in 2:ncol(out2)) out2[,i] &lt;- round(as.numeric(out2[,i]),3)
for(i in 5) out2[,i] &lt;- paste(&quot;(&quot;,out2[,i],&quot;)&quot;,sep=&quot;&quot;)

# Assemble results

itt.2g.tab &lt;- cbind(out2, cbind(apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, round(x[1],3) ,&quot;, &quot;, round(x[2],3), &quot;]&quot;, sep=&quot;&quot;))))
colnames(itt.2g.tab) &lt;- c(&quot;Outcome&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;t&quot;, &quot;p-value&quot;, &quot;95% CI&quot;)

# Save results
write.csv(itt.2g.tab, &quot;out_tablea7_itt_2g_blockfe_nocovs_UNFORMATTED.csv&quot;, row.names=FALSE)

kable(itt.2g.tab[1:9,], caption=&quot;**Table A7, Panel I. Monitoring vs. Control**&quot;)</code></pre>
<table>
<caption><strong>Table A7, Panel I. Monitoring vs. Control</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">-0.015</td>
<td align="left">0.052</td>
<td align="left">-0.285</td>
<td align="left">(0.388)</td>
<td align="left">[-0.117, 0.087]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">-0.002</td>
<td align="left">0.045</td>
<td align="left">-0.035</td>
<td align="left">(0.486)</td>
<td align="left">[-0.091, 0.088]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">-0.003</td>
<td align="left">0.036</td>
<td align="left">-0.091</td>
<td align="left">(0.464)</td>
<td align="left">[-0.075, 0.068]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">-0.061</td>
<td align="left">0.057</td>
<td align="left">-1.079</td>
<td align="left">(0.141)</td>
<td align="left">[-0.172, 0.05]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.036</td>
<td align="left">0.043</td>
<td align="left">-0.837</td>
<td align="left">(0.201)</td>
<td align="left">[-0.121, 0.049]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.017</td>
<td align="left">0.034</td>
<td align="left">-0.496</td>
<td align="left">(0.31)</td>
<td align="left">[-0.084, 0.05]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">-0.089</td>
<td align="left">0.052</td>
<td align="left">-1.7</td>
<td align="left">(0.09)</td>
<td align="left">[-0.192, 0.014]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.035</td>
<td align="left">0.042</td>
<td align="left">-0.822</td>
<td align="left">(0.412)</td>
<td align="left">[-0.118, 0.048]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.014</td>
<td align="left">0.031</td>
<td align="left">-0.444</td>
<td align="left">(0.657)</td>
<td align="left">[-0.074, 0.047]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(itt.2g.tab[10:18,], caption=&quot;**Table A7, Panel II. Punitive vs. Control**&quot;)</code></pre>
<table>
<caption><strong>Table A7, Panel II. Punitive vs. Control</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.007</td>
<td align="left">0.053</td>
<td align="left">0.139</td>
<td align="left">(0.555)</td>
<td align="left">[-0.097, 0.112]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.019</td>
<td align="left">0.042</td>
<td align="left">0.456</td>
<td align="left">(0.676)</td>
<td align="left">[-0.064, 0.103]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.018</td>
<td align="left">0.035</td>
<td align="left">0.515</td>
<td align="left">(0.697)</td>
<td align="left">[-0.051, 0.087]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.048</td>
<td align="left">0.055</td>
<td align="left">0.863</td>
<td align="left">(0.806)</td>
<td align="left">[-0.061, 0.157]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.066</td>
<td align="left">0.041</td>
<td align="left">-1.596</td>
<td align="left">(0.056)</td>
<td align="left">[-0.147, 0.015]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.021</td>
<td align="left">0.034</td>
<td align="left">-0.618</td>
<td align="left">(0.268)</td>
<td align="left">[-0.089, 0.046]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">0.039</td>
<td align="left">0.049</td>
<td align="left">0.791</td>
<td align="left">(0.429)</td>
<td align="left">[-0.057, 0.134]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.085</td>
<td align="left">0.041</td>
<td align="left">-2.097</td>
<td align="left">(0.037)</td>
<td align="left">[-0.165, -0.005]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.039</td>
<td align="left">0.033</td>
<td align="left">-1.172</td>
<td align="left">(0.242)</td>
<td align="left">[-0.105, 0.027]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(itt.2g.tab[19:27,], caption=&quot;**Table A7, Panel III. Punitive vs. Monitoring**&quot;)</code></pre>
<table>
<caption><strong>Table A7, Panel III. Punitive vs. Monitoring</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.018</td>
<td align="left">0.058</td>
<td align="left">0.319</td>
<td align="left">(0.75)</td>
<td align="left">[-0.095, 0.132]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.033</td>
<td align="left">0.049</td>
<td align="left">0.665</td>
<td align="left">(0.506)</td>
<td align="left">[-0.064, 0.129]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.026</td>
<td align="left">0.037</td>
<td align="left">0.685</td>
<td align="left">(0.494)</td>
<td align="left">[-0.048, 0.099]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.107</td>
<td align="left">0.063</td>
<td align="left">1.701</td>
<td align="left">(0.09)</td>
<td align="left">[-0.017, 0.231]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.019</td>
<td align="left">0.049</td>
<td align="left">-0.391</td>
<td align="left">(0.696)</td>
<td align="left">[-0.116, 0.078]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.002</td>
<td align="left">0.038</td>
<td align="left">0.047</td>
<td align="left">(0.962)</td>
<td align="left">[-0.073, 0.077]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">0.139</td>
<td align="left">0.057</td>
<td align="left">2.431</td>
<td align="left">(0.016)</td>
<td align="left">[0.026, 0.251]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.052</td>
<td align="left">0.047</td>
<td align="left">-1.11</td>
<td align="left">(0.268)</td>
<td align="left">[-0.144, 0.04]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.024</td>
<td align="left">0.036</td>
<td align="left">-0.667</td>
<td align="left">(0.505)</td>
<td align="left">[-0.094, 0.046]</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 17:10:26 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrr}
##   \hline
## Outcome &amp; Estimate &amp; SE &amp; t &amp; p-value &amp; 95\% CI \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; -0.015 &amp; 0.052 &amp; -0.285 &amp; (0.388) &amp; [-0.117, 0.087] \\ 
##   Received post-visit callback (White vs. Black) &amp; -0.002 &amp; 0.045 &amp; -0.035 &amp; (0.486) &amp; [-0.091, 0.088] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; -0.003 &amp; 0.036 &amp; -0.091 &amp; (0.464) &amp; [-0.075, 0.068] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; -0.061 &amp; 0.057 &amp; -1.079 &amp; (0.141) &amp; [-0.172, 0.05] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.036 &amp; 0.043 &amp; -0.837 &amp; (0.201) &amp; [-0.121, 0.049] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.017 &amp; 0.034 &amp; -0.496 &amp; (0.31) &amp; [-0.084, 0.05] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; -0.089 &amp; 0.052 &amp; -1.7 &amp; (0.09) &amp; [-0.192, 0.014] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.035 &amp; 0.042 &amp; -0.822 &amp; (0.412) &amp; [-0.118, 0.048] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.014 &amp; 0.031 &amp; -0.444 &amp; (0.657) &amp; [-0.074, 0.047] \\ 
##   II. Punitive vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.007 &amp; 0.053 &amp; 0.139 &amp; (0.555) &amp; [-0.097, 0.112] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.019 &amp; 0.042 &amp; 0.456 &amp; (0.676) &amp; [-0.064, 0.103] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.018 &amp; 0.035 &amp; 0.515 &amp; (0.697) &amp; [-0.051, 0.087] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.048 &amp; 0.055 &amp; 0.863 &amp; (0.806) &amp; [-0.061, 0.157] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.066 &amp; 0.041 &amp; -1.596 &amp; (0.056) &amp; [-0.147, 0.015] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.021 &amp; 0.034 &amp; -0.618 &amp; (0.268) &amp; [-0.089, 0.046] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; 0.039 &amp; 0.049 &amp; 0.791 &amp; (0.429) &amp; [-0.057, 0.134] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.085 &amp; 0.041 &amp; -2.097 &amp; (0.037) &amp; [-0.165, -0.005] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.039 &amp; 0.033 &amp; -1.172 &amp; (0.242) &amp; [-0.105, 0.027] \\ 
##   III. Punitive vs. Monitoring &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.018 &amp; 0.058 &amp; 0.319 &amp; (0.75) &amp; [-0.095, 0.132] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.033 &amp; 0.049 &amp; 0.665 &amp; (0.506) &amp; [-0.064, 0.129] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.026 &amp; 0.037 &amp; 0.685 &amp; (0.494) &amp; [-0.048, 0.099] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.107 &amp; 0.063 &amp; 1.701 &amp; (0.09) &amp; [-0.017, 0.231] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.019 &amp; 0.049 &amp; -0.391 &amp; (0.696) &amp; [-0.116, 0.078] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; 0.002 &amp; 0.038 &amp; 0.047 &amp; (0.962) &amp; [-0.073, 0.077] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; 0.139 &amp; 0.057 &amp; 2.431 &amp; (0.016) &amp; [0.026, 0.251] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.052 &amp; 0.047 &amp; -1.11 &amp; (0.268) &amp; [-0.144, 0.04] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.024 &amp; 0.036 &amp; -0.667 &amp; (0.505) &amp; [-0.094, 0.046] \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a8-p.a-26-unweighted-itt-estimates-of-messaging-on-net-discrimination-in-receiving-a-post-visit-callback" class="section level2">
<h2><span class="header-section-number">2.11</span> Table A8 (p. A-26): Unweighted ITT Estimates of Messaging on Net Discrimination in Receiving a Post-Visit Callback</h2>
<pre class="r"><code># top left
cb1 &lt;- ddply(dat, .(TA), summarise,
              mean.cb.w = mean(cb_C),
              mean.cb.b = mean(cb_A),
              mean.cb.h = mean(cb_B))
cb1 &lt;- t(cb1[,2:4])
colnames(cb1) &lt;- c(&quot;Control&quot;,&quot;Monitoring&quot;,&quot;Punitive&quot;)

# bottom left
cb2 &lt;- ddply(dat, .(TA), summarise,
             mean.ncb.wb = mean(ncb_wb),
             mean.ncb.wh = mean(ncb_wh),
             mean.ncb.bh = mean(ncb_bh))
cb2 &lt;- t(cb2[,2:4])

# top right
cb3 &lt;- cbind(cb1[,2]-cb1[,1], cb1[,3]-cb1[,1], cb1[,3]-cb1[,2])

# bottom right
cb4 &lt;- cbind(cb2[,2]-cb2[,1], cb2[,3]-cb2[,1], cb2[,3]-cb2[,2])

cb1 &lt;- round(cb1,3)
cb2 &lt;- round(cb2,3)
cb3 &lt;- round(cb3,3)
cb4 &lt;- round(cb4,3)

# p values
get_p &lt;- function(var1, var2, T_r, T_c){
  ps &lt;- c(t.test(dat[dat$TA==0, var1],
                 dat[dat$TA==0, var2], 
                 alternative=&quot;two.sided&quot;,
                 paired=TRUE,
                 conf.level=0.95)$p.value,
          t.test(dat[dat$TA==1, var1],
                 dat[dat$TA==1, var2],
                 alternative=&quot;two.sided&quot;,
                 paired=TRUE,
                 conf.level=0.95)$p.value,
          t.test(dat[dat$TA==2, var1],
                 dat[dat$TA==2, var2], 
                 alternative=&quot;two.sided&quot;,
                 paired=TRUE,
                 conf.level=0.95)$p.value)
  return(ps)
  }

p2 &lt;- rbind(get_p(&quot;cb_C&quot;,&quot;cb_A&quot;),
            get_p(&quot;cb_C&quot;,&quot;cb_B&quot;),
            get_p(&quot;cb_A&quot;,&quot;cb_B&quot;))

p2 &lt;- matrix(paste(&quot;(&quot;,round(p2,3),&quot;)&quot;,sep=&quot;&quot;), ncol=3, byrow=FALSE)

#p-value within race across treatments
get_p2 &lt;- function(var){
  ps &lt;- c(t.test(dat[dat$TA==1, var],
                 dat[dat$TA==0, var], 
                 alternative=&quot;two.sided&quot;,
                 conf.level=0.95)$p.value,
          t.test(dat[dat$TA==2, var],
                 dat[dat$TA==0, var],
                 alternative=&quot;two.sided&quot;,
                 conf.level=0.95)$p.value,
          t.test(dat[dat$TA==2, var],
                 dat[dat$TA==1, var], 
                 alternative=&quot;two.sided&quot;,
                 conf.level=0.95)$p.value)
  return(ps)
}

p3 &lt;- rbind(get_p2(&quot;cb_C&quot;), get_p2(&quot;cb_A&quot;), get_p2(&quot;cb_B&quot;))
p3 &lt;- matrix(paste(&quot;(&quot;,round(p3,3),&quot;)&quot;,sep=&quot;&quot;), ncol=3, byrow=FALSE)

f0 &lt;- lm(ncb_wb ~ TA1 + TA2, data=dat)
f1 &lt;- lm(ncb_wb ~ TA0 + TA2, data=dat)
p4.wb &lt;- c(pt(q=summary(f0)$coefficients[,3], df=f0$df.residual, lower.tail=TRUE)[2:3],
           summary(f1)$coefficients[3,4])

f0 &lt;- lm(ncb_wh ~ TA1 + TA2, data=dat)
f1 &lt;- lm(ncb_wh ~ TA0 + TA2, data=dat)
p4.wh &lt;- c(pt(q=summary(f0)$coefficients[,3], df=f0$df.residual, lower.tail=TRUE)[2:3],
           summary(f1)$coefficients[3,4])

f0 &lt;- lm(ncb_bh ~ TA1 + TA2, data=dat)
f1 &lt;- lm(ncb_bh ~ TA0 + TA2, data=dat)
p4.bh &lt;- c(summary(f0)$coefficients[2:3,4],
           summary(f1)$coefficients[3,4])

p4 &lt;- rbind(p4.wb, p4.wh, p4.bh)
p4 &lt;- matrix(paste(&quot;(&quot;,round(p4,3),&quot;)&quot;,sep=&quot;&quot;), ncol=3, byrow=FALSE)

p1 &lt;- matrix(NA, ncol=3, nrow=3)

cb1 &lt;- rbind(cb1[1,], p1[1,],
             cb1[2,], p1[2,],
             cb1[3,], p1[3,])
cb2 &lt;- rbind(cb2[1,], p2[1,],
             cb2[2,], p2[2,],
             cb2[3,], p2[3,])
cb3 &lt;- rbind(cb3[1,], p3[1,],
             cb3[2,], p3[2,],
             cb3[3,], p3[3,])
cb4 &lt;- rbind(cb4[1,], p4[1,],
             cb4[2,], p4[2,],
             cb4[3,], p4[3,])

table_a8 &lt;- cbind(c(&quot;White&quot;,NA,&quot;Black&quot;,NA,&quot;Hispanic&quot;,NA,&quot;White vs. Black&quot;,NA,&quot;White vs. Hispanic&quot;,NA,&quot;Black vs. Hispanic&quot;,NA),
             rbind(cbind(cb1, cb3), cbind(cb2, cb4)))
table_a8 &lt;- rbind(table_a8, 
                  c(&quot;Sample size&quot;, nrow(dat[dat$TA == 0,]), nrow(dat[dat$TA == 1,]), 
                       nrow(dat[dat$TA == 2,]), nrow(dat[dat$TA %in% c(0,1),]), 
                       nrow(dat[dat$TA %in% c(0,2),]), nrow(dat[dat$TA %in% c(1,2),])))

colnames(table_a8) &lt;- c(&quot;&quot;,&quot;Control&quot;, &quot;Monitoring&quot;, &quot;Punitive&quot;, &quot;Monitoring vs Control&quot;, &quot;Punitive vs Control&quot;, &quot;Punitive vs Monitoring&quot;)

print(xtable(table_a8), file=&quot;out_tablea8_unweighted_itt_callbacks.tex&quot;, include.rownames=FALSE)
kable(table_a8[1:6,], caption=&quot;**Table A8, Panel A. Percent Favorable**&quot;)</code></pre>
<table>
<caption><strong>Table A8, Panel A. Percent Favorable</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Control</th>
<th align="left">Monitoring</th>
<th align="left">Punitive</th>
<th align="left">Monitoring vs Control</th>
<th align="left">Punitive vs Control</th>
<th align="left">Punitive vs Monitoring</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>White</td>
<td align="left">0.215</td>
<td align="left">0.184</td>
<td align="left">0.19</td>
<td align="left">-0.031</td>
<td align="left">-0.025</td>
<td align="left">0.006</td>
</tr>
<tr class="even">
<td></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">(0.418)</td>
<td align="left">(0.5)</td>
<td align="left">(0.881)</td>
</tr>
<tr class="odd">
<td>Black</td>
<td align="left">0.168</td>
<td align="left">0.144</td>
<td align="left">0.12</td>
<td align="left">-0.025</td>
<td align="left">-0.048</td>
<td align="left">-0.024</td>
</tr>
<tr class="even">
<td></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">(0.478)</td>
<td align="left">(0.133)</td>
<td align="left">(0.502)</td>
</tr>
<tr class="odd">
<td>Hispanic</td>
<td align="left">0.154</td>
<td align="left">0.155</td>
<td align="left">0.185</td>
<td align="left">0.001</td>
<td align="left">0.031</td>
<td align="left">0.03</td>
</tr>
<tr class="even">
<td></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">(0.976)</td>
<td align="left">(0.378)</td>
<td align="left">(0.444)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(table_a8[7:12,], caption=&quot;**Table A8, Panel B. Net Discrimination (% Majority Favorable - % Minority Favorable)**&quot;)</code></pre>
<table>
<caption><strong>Table A8, Panel B. Net Discrimination (% Majority Favorable - % Minority Favorable)</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Control</th>
<th align="left">Monitoring</th>
<th align="left">Punitive</th>
<th align="left">Monitoring vs Control</th>
<th align="left">Punitive vs Control</th>
<th align="left">Punitive vs Monitoring</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>White vs. Black</td>
<td align="left">0.047</td>
<td align="left">0.04</td>
<td align="left">0.07</td>
<td align="left">-0.006</td>
<td align="left">0.023</td>
<td align="left">0.03</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(0.107)</td>
<td align="left">(0.264)</td>
<td align="left">(0.026)</td>
<td align="left">(0.444)</td>
<td align="left">(0.705)</td>
<td align="left">(0.539)</td>
</tr>
<tr class="odd">
<td>White vs. Hispanic</td>
<td align="left">0.061</td>
<td align="left">0.029</td>
<td align="left">0.005</td>
<td align="left">-0.032</td>
<td align="left">-0.056</td>
<td align="left">-0.024</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(0.019)</td>
<td align="left">(0.425)</td>
<td align="left">(0.876)</td>
<td align="left">(0.23)</td>
<td align="left">(0.09)</td>
<td align="left">(0.611)</td>
</tr>
<tr class="odd">
<td>Black vs. Hispanic</td>
<td align="left">0.014</td>
<td align="left">-0.011</td>
<td align="left">-0.065</td>
<td align="left">-0.026</td>
<td align="left">-0.079</td>
<td align="left">-0.054</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(0.587)</td>
<td align="left">(0.733)</td>
<td align="left">(0.037)</td>
<td align="left">(0.544)</td>
<td align="left">(0.052)</td>
<td align="left">(0.242)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(rbind(table_a8[13,]), caption=&quot;**Table A8. Sample Size (last row)**&quot;)</code></pre>
<table>
<caption><strong>Table A8. Sample Size (last row)</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Control</th>
<th align="left">Monitoring</th>
<th align="left">Punitive</th>
<th align="left">Monitoring vs Control</th>
<th align="left">Punitive vs Control</th>
<th align="left">Punitive vs Monitoring</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Sample size</td>
<td align="left">279</td>
<td align="left">174</td>
<td align="left">200</td>
<td align="left">453</td>
<td align="left">479</td>
<td align="left">374</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a9-p.a-27-unweighted-itt-estimates-of-messaging-on-net-discrimination-in-receiving-a-post-visit-offer-for-the-unit" class="section level2">
<h2><span class="header-section-number">2.12</span> Table A9 (p. A-27): Unweighted ITT Estimates of Messaging on Net Discrimination in Receiving a Post-Visit Offer for the Unit</h2>
<pre class="r"><code># top left
off1 &lt;- ddply(dat, .(TA), summarise,
              mean.off.w = mean(off_C),
              mean.off.b = mean(off_A),
              mean.off.h = mean(off_B))
off1 &lt;- t(off1[,2:4])
colnames(off1) &lt;- c(&quot;Control&quot;,&quot;Monitoring&quot;,&quot;Punitive&quot;)

# bottom left
off2 &lt;- ddply(dat, .(TA), summarise,
              mean.noff.wb = mean(noff_wb),
              mean.noff.wh = mean(noff_wh),
              mean.noff.bh = mean(noff_bh))
off2 &lt;- t(off2[,2:4])

# top right
off3 &lt;- cbind(off1[,2]-off1[,1], off1[,3]-off1[,1], off1[,3]-off1[,2])

# bottom right
off4 &lt;- cbind(off2[,2]-off2[,1], off2[,3]-off2[,1], off2[,3]-off2[,2])

off1 &lt;- round(off1,3)
off2 &lt;- round(off2,3)
off3 &lt;- round(off3,3)
off4 &lt;- round(off4,3)

# p values

p2 &lt;- rbind(get_p(&quot;off_C&quot;,&quot;off_A&quot;),
            get_p(&quot;off_C&quot;,&quot;off_B&quot;),
            get_p(&quot;off_A&quot;,&quot;off_B&quot;))

p2 &lt;- matrix(paste(&quot;(&quot;,round(p2,3),&quot;)&quot;,sep=&quot;&quot;), ncol=3, byrow=FALSE)

p3 &lt;- rbind(get_p2(&quot;off_C&quot;),
            get_p2(&quot;off_A&quot;),
            get_p2(&quot;off_B&quot;))

p3 &lt;- matrix(paste(&quot;(&quot;,round(p3,3),&quot;)&quot;,sep=&quot;&quot;), ncol=3, byrow=FALSE)

f0 &lt;- lm(noff_wb ~ TA1 + TA2, data=dat)
f1 &lt;- lm(noff_wb ~ TA0 + TA2, data=dat)
p4.wb &lt;- c(pt(q=summary(f0)$coefficients[,3], df=f0$df.residual, lower.tail=TRUE)[2:3],
           summary(f1)$coefficients[3,4])

f0 &lt;- lm(noff_wh ~ TA1 + TA2, data=dat)
f1 &lt;- lm(noff_wh ~ TA0 + TA2, data=dat)
p4.wh &lt;- c(pt(q=summary(f0)$coefficients[,3], df=f0$df.residual, lower.tail=TRUE)[2:3],
           summary(f1)$coefficients[3,4])

f0 &lt;- lm(noff_bh ~ TA1 + TA2, data=dat)
f1 &lt;- lm(noff_bh ~ TA0 + TA2, data=dat)
p4.bh &lt;- c(summary(f0)$coefficients[2:3,4],
           summary(f1)$coefficients[3,4])

p4 &lt;- rbind(p4.wb, p4.wh, p4.bh)
p4 &lt;- matrix(paste(&quot;(&quot;,round(p4,3),&quot;)&quot;,sep=&quot;&quot;), ncol=3, byrow=FALSE)

p1 &lt;- matrix(NA, ncol=3, nrow=3)

off1 &lt;- rbind(off1[1,], p1[1,],
              off1[2,], p1[2,],
              off1[3,], p1[3,])
off2 &lt;- rbind(off2[1,], p2[1,],
              off2[2,], p2[2,],
              off2[3,], p2[3,])
off3 &lt;- rbind(off3[1,], p3[1,],
              off3[2,], p3[2,],
              off3[3,], p3[3,])
off4 &lt;- rbind(off4[1,], p4[1,],
              off4[2,], p4[2,],
              off4[3,], p4[3,])

table_a9 &lt;- cbind(c(&quot;White&quot;,NA,&quot;Black&quot;,NA,&quot;Hispanic&quot;,NA,&quot;White vs. Black&quot;,NA,&quot;White vs. Hispanic&quot;,NA,&quot;Black vs. Hispanic&quot;,NA),
             rbind(cbind(off1, off3), cbind(off2, off4)))
table_a9 &lt;- rbind(table_a9, 
                  c(&quot;Sample size&quot;, nrow(dat[dat$TA == 0,]), nrow(dat[dat$TA == 1,]), 
                       nrow(dat[dat$TA == 2,]), nrow(dat[dat$TA %in% c(0,1),]), 
                       nrow(dat[dat$TA %in% c(0,2),]), nrow(dat[dat$TA %in% c(1,2),])))
colnames(table_a9) &lt;- c(&quot;&quot;,&quot;Control&quot;, &quot;Monitoring&quot;, &quot;Punitive&quot;, &quot;Monitoring vs Control&quot;, &quot;Punitive vs Control&quot;, &quot;Punitive vs Monitoring&quot;)

print(xtable(table_a9), file=&quot;out_tablea9_unweighted_itt_offers.tex&quot;, include.rownames=FALSE)
kable(table_a9[1:6,], caption=&quot;**Table A9, Panel A. Percent Favorable**&quot;)</code></pre>
<table>
<caption><strong>Table A9, Panel A. Percent Favorable</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Control</th>
<th align="left">Monitoring</th>
<th align="left">Punitive</th>
<th align="left">Monitoring vs Control</th>
<th align="left">Punitive vs Control</th>
<th align="left">Punitive vs Monitoring</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>White</td>
<td align="left">0.118</td>
<td align="left">0.08</td>
<td align="left">0.105</td>
<td align="left">-0.038</td>
<td align="left">-0.013</td>
<td align="left">0.025</td>
</tr>
<tr class="even">
<td></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">(0.183)</td>
<td align="left">(0.648)</td>
<td align="left">(0.414)</td>
</tr>
<tr class="odd">
<td>Black</td>
<td align="left">0.09</td>
<td align="left">0.08</td>
<td align="left">0.08</td>
<td align="left">-0.009</td>
<td align="left">-0.01</td>
<td align="left">0</td>
</tr>
<tr class="even">
<td></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">(0.734)</td>
<td align="left">(0.709)</td>
<td align="left">(0.987)</td>
</tr>
<tr class="odd">
<td>Hispanic</td>
<td align="left">0.061</td>
<td align="left">0.063</td>
<td align="left">0.095</td>
<td align="left">0.002</td>
<td align="left">0.034</td>
<td align="left">0.032</td>
</tr>
<tr class="even">
<td></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">(0.922)</td>
<td align="left">(0.178)</td>
<td align="left">(0.254)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(table_a9[7:12,], caption=&quot;**Table A9, Panel B. Net Discrimination (% Majority Favorable - % Minority Favorable)**&quot;)</code></pre>
<table>
<caption><strong>Table A9, Panel B. Net Discrimination (% Majority Favorable - % Minority Favorable)</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Control</th>
<th align="left">Monitoring</th>
<th align="left">Punitive</th>
<th align="left">Monitoring vs Control</th>
<th align="left">Punitive vs Control</th>
<th align="left">Punitive vs Monitoring</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>White vs. Black</td>
<td align="left">0.029</td>
<td align="left">0</td>
<td align="left">0.025</td>
<td align="left">-0.029</td>
<td align="left">-0.004</td>
<td align="left">0.025</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(0.239)</td>
<td align="left">(1)</td>
<td align="left">(0.319)</td>
<td align="left">(0.216)</td>
<td align="left">(0.458)</td>
<td align="left">(0.523)</td>
</tr>
<tr class="odd">
<td>White vs. Hispanic</td>
<td align="left">0.057</td>
<td align="left">0.017</td>
<td align="left">0.01</td>
<td align="left">-0.04</td>
<td align="left">-0.047</td>
<td align="left">-0.007</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(0.011)</td>
<td align="left">(0.514)</td>
<td align="left">(0.706)</td>
<td align="left">(0.13)</td>
<td align="left">(0.083)</td>
<td align="left">(0.85)</td>
</tr>
<tr class="odd">
<td>Black vs. Hispanic</td>
<td align="left">0.029</td>
<td align="left">0.017</td>
<td align="left">-0.015</td>
<td align="left">-0.011</td>
<td align="left">-0.044</td>
<td align="left">-0.032</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(0.17)</td>
<td align="left">(0.44)</td>
<td align="left">(0.565)</td>
<td align="left">(0.729)</td>
<td align="left">(0.168)</td>
<td align="left">(0.362)</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(rbind(table_a9[13,]), caption=&quot;**Table A9. Sample Size (last row)**&quot;)</code></pre>
<table>
<caption><strong>Table A9. Sample Size (last row)</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Control</th>
<th align="left">Monitoring</th>
<th align="left">Punitive</th>
<th align="left">Monitoring vs Control</th>
<th align="left">Punitive vs Control</th>
<th align="left">Punitive vs Monitoring</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Sample size</td>
<td align="left">279</td>
<td align="left">174</td>
<td align="left">200</td>
<td align="left">453</td>
<td align="left">479</td>
<td align="left">374</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a10-p.a-28-sensitivity-analysis-estimated-effects-of-messaging-on-net-discrimination-levels-three-group-parametric-estimator" class="section level2">
<h2><span class="header-section-number">2.13</span> Table A10 (p. A-28): Sensitivity Analysis: Estimated Effects of Messaging on Net Discrimination Levels (Three-Group Parametric Estimator)</h2>
<pre class="r"><code>  #one-sided p function
  get_plower &lt;- function(x) pt(summary(x)$coefficients[,3], summary(x)$df[2], lower = TRUE)
  
  # select all outcome variables
  outpatt &lt;- c(&quot;^index&quot;,&quot;ncb&quot;,&quot;noff&quot;)
  outvar &lt;- unlist(lapply(outpatt, function(y) grep(y, names(dat), value = TRUE)))
  
  datm &lt;- reshape2::melt(dat, id = names(dat)[!names(dat)%in% c(outvar)],
                         variable.name = &quot;outcome&quot;, 
                         value.name = &quot;outcome_val&quot;)

  # control as comparison

  # monitoring vs. control
  dat_out_c &lt;- datm %&gt;% 
    group_by(outcome) %&gt;%
    do(fit = lm(outcome_val ~ TA1 + TA2 + as.factor(block), weights=ipw, data=.))

  dat_outcome_coef &lt;- tidy(dat_out_c, fit)

  #one-sided p-value
  dat_out_c$p_lower &lt;- lapply(dat_out_c$fit, get_plower)
  p_lower &lt;- tidy(dat_out_c, p_lower) %&gt;% dplyr::rename(term = names) %&gt;% dplyr::rename(p.lower = x)

  dat_coef &lt;- left_join(dat_outcome_coef, p_lower, by = c(&quot;outcome&quot;,&quot;term&quot;))

  # replace with one-sided p.value for all but B-H comparison
  dat_coef$p.value[grep(&quot;bh$&quot;, dat_coef$outcome, invert = T)] &lt;- dat_coef$p.lower[grep(&quot;bh$&quot;, dat_coef$outcome, invert = T)]

  itt3g_mc &lt;- dat_coef[dat_coef$term == &quot;TA1&quot;,] %&gt;% dplyr::select(-p.lower, -term)
  itt3g_mc$df &lt;- unlist(lapply(dat_out_c$fit, function(x) summary(x)$df[2]))

  # punitive vs. control
  itt3g_pc &lt;- dat_coef[dat_coef$term == &quot;TA2&quot;,] %&gt;% dplyr::select(-p.lower, -term)
  itt3g_pc$df &lt;- unlist(lapply(dat_out_c$fit, function(x) summary(x)$df[2]))
  
  # monitoring as comparison

  # punitive vs monitoring
  dat_out_m &lt;- datm %&gt;%
    group_by(outcome) %&gt;%
    do(fit = lm(outcome_val ~ TA0 + TA2 + as.factor(block), weights=ipw, data=.))

  dat_outcome_coef &lt;- tidy(dat_out_m, fit)

  #one-sided p-value
  dat_out_m$p_lower &lt;- lapply(dat_out_m$fit, get_plower)
  p_lower &lt;- tidy(dat_out_m, p_lower) %&gt;% rename(term = names) %&gt;% rename(p.lower = x)

  dat_coef &lt;- left_join(dat_outcome_coef, p_lower, by = c(&quot;outcome&quot;,&quot;term&quot;))
  itt3g_pm &lt;- dat_coef[dat_coef$term == &quot;TA2&quot;,] %&gt;% dplyr::select(-term, -p.lower)
  itt3g_pm$df &lt;- unlist(lapply(dat_out_m$fit, function(x) summary(x)$df[2]))

  r_ord &lt;- c(7,2,5,8,3,6,9,1,4)

  out3 &lt;- rbind(itt3g_mc[r_ord,], itt3g_pc[r_ord,], itt3g_pm[r_ord,])
  out3$outcome &lt;- rep(c(outvars.wb.labs[-1], outvars.wh.labs[-1], outvars.bh.labs[-1]), 3)

itt.3g.ci &lt;- as.numeric(out3$estimate) + abs(qt(p=0.025, df=out3$df, lower.tail=TRUE))*cbind(-as.numeric(out3$std.error), as.numeric(out3$std.error))
itt.3g.cir &lt;- apply(itt.3g.ci, 1, function(x) paste(&quot;[&quot;, round(x[1],3) ,&quot;, &quot;, round(x[2],3), &quot;]&quot;, sep=&quot;&quot;))

# Join with CI
itt.3g.tab &lt;- cbind(as.matrix(out3[,-6]), itt.3g.cir)

# Round and format results
for(i in 2:5) itt.3g.tab[,i] &lt;- round(as.numeric(itt.3g.tab[,i]), 3)
itt.3g.tab[,5] &lt;- paste(&quot;(&quot;,itt.3g.tab[,5],&quot;)&quot;,sep=&quot;&quot;)

colnames(itt.3g.tab) &lt;- c(&quot;Outcome&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;t&quot;, &quot;p-value&quot;, &quot;95% CI&quot;)
write.csv(itt.3g.tab, file=&quot;out_tablea10_itt_blockfe_nocovs.csv&quot;, row.names=FALSE)

kable(itt.3g.tab[1:9,], caption=&quot;**Table A10, Panel I. Monitoring vs. Control**&quot;)</code></pre>
<table>
<caption><strong>Table A10, Panel I. Monitoring vs. Control</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">-0.012</td>
<td align="left">0.054</td>
<td align="left">-0.225</td>
<td align="left">(0.411)</td>
<td align="left">[-0.119, 0.094]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">-0.009</td>
<td align="left">0.046</td>
<td align="left">-0.189</td>
<td align="left">(0.425)</td>
<td align="left">[-0.099, 0.081]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">-0.007</td>
<td align="left">0.036</td>
<td align="left">-0.194</td>
<td align="left">(0.423)</td>
<td align="left">[-0.079, 0.065]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">-0.059</td>
<td align="left">0.059</td>
<td align="left">-0.999</td>
<td align="left">(0.159)</td>
<td align="left">[-0.175, 0.057]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.044</td>
<td align="left">0.045</td>
<td align="left">-0.987</td>
<td align="left">(0.162)</td>
<td align="left">[-0.133, 0.044]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.023</td>
<td align="left">0.036</td>
<td align="left">-0.654</td>
<td align="left">(0.257)</td>
<td align="left">[-0.094, 0.047]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">-0.092</td>
<td align="left">0.053</td>
<td align="left">-1.745</td>
<td align="left">(0.082)</td>
<td align="left">[-0.197, 0.012]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.036</td>
<td align="left">0.043</td>
<td align="left">-0.822</td>
<td align="left">(0.411)</td>
<td align="left">[-0.121, 0.05]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.016</td>
<td align="left">0.033</td>
<td align="left">-0.489</td>
<td align="left">(0.625)</td>
<td align="left">[-0.082, 0.049]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(itt.3g.tab[10:18,], caption=&quot;**Table A10, Panel II. Punitive vs. Control**&quot;)</code></pre>
<table>
<caption><strong>Table A10, Panel II. Punitive vs. Control</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.01</td>
<td align="left">0.052</td>
<td align="left">0.198</td>
<td align="left">(0.579)</td>
<td align="left">[-0.092, 0.112]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.02</td>
<td align="left">0.044</td>
<td align="left">0.454</td>
<td align="left">(0.675)</td>
<td align="left">[-0.067, 0.107]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.016</td>
<td align="left">0.035</td>
<td align="left">0.465</td>
<td align="left">(0.679)</td>
<td align="left">[-0.053, 0.086]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.05</td>
<td align="left">0.056</td>
<td align="left">0.893</td>
<td align="left">(0.814)</td>
<td align="left">[-0.06, 0.16]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.068</td>
<td align="left">0.043</td>
<td align="left">-1.568</td>
<td align="left">(0.059)</td>
<td align="left">[-0.153, 0.017]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.025</td>
<td align="left">0.035</td>
<td align="left">-0.72</td>
<td align="left">(0.236)</td>
<td align="left">[-0.093, 0.043]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">0.039</td>
<td align="left">0.05</td>
<td align="left">0.781</td>
<td align="left">(0.435)</td>
<td align="left">[-0.06, 0.138]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.088</td>
<td align="left">0.042</td>
<td align="left">-2.1</td>
<td align="left">(0.036)</td>
<td align="left">[-0.171, -0.006]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.041</td>
<td align="left">0.032</td>
<td align="left">-1.278</td>
<td align="left">(0.202)</td>
<td align="left">[-0.105, 0.022]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(itt.3g.tab[19:27,], caption=&quot;**Table A10, Panel III. Punitive vs. Monitoring**&quot;)</code></pre>
<table>
<caption><strong>Table A10, Panel III. Punitive vs. Monitoring</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.022</td>
<td align="left">0.055</td>
<td align="left">0.412</td>
<td align="left">(0.68)</td>
<td align="left">[-0.085, 0.13]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.029</td>
<td align="left">0.046</td>
<td align="left">0.631</td>
<td align="left">(0.528)</td>
<td align="left">[-0.061, 0.118]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.023</td>
<td align="left">0.036</td>
<td align="left">0.647</td>
<td align="left">(0.518)</td>
<td align="left">[-0.048, 0.095]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.109</td>
<td align="left">0.058</td>
<td align="left">1.879</td>
<td align="left">(0.061)</td>
<td align="left">[-0.005, 0.223]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.024</td>
<td align="left">0.045</td>
<td align="left">-0.532</td>
<td align="left">(0.595)</td>
<td align="left">[-0.111, 0.064]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.001</td>
<td align="left">0.036</td>
<td align="left">-0.042</td>
<td align="left">(0.966)</td>
<td align="left">[-0.071, 0.068]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">0.132</td>
<td align="left">0.053</td>
<td align="left">2.48</td>
<td align="left">(0.014)</td>
<td align="left">[0.027, 0.236]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.053</td>
<td align="left">0.043</td>
<td align="left">-1.216</td>
<td align="left">(0.224)</td>
<td align="left">[-0.137, 0.032]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.025</td>
<td align="left">0.033</td>
<td align="left">-0.752</td>
<td align="left">(0.452)</td>
<td align="left">[-0.09, 0.04]</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 17:10:28 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrr}
##   \hline
## Outcome &amp; Estimate &amp; SE &amp; t &amp; p-value &amp; 95\% CI \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; -0.015 &amp; 0.052 &amp; -0.285 &amp; (0.388) &amp; [-0.117, 0.087] \\ 
##   Received post-visit callback (White vs. Black) &amp; -0.002 &amp; 0.045 &amp; -0.035 &amp; (0.486) &amp; [-0.091, 0.088] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; -0.003 &amp; 0.036 &amp; -0.091 &amp; (0.464) &amp; [-0.075, 0.068] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; -0.059 &amp; 0.059 &amp; -0.999 &amp; (0.159) &amp; [-0.175, 0.057] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.044 &amp; 0.045 &amp; -0.987 &amp; (0.162) &amp; [-0.133, 0.044] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.023 &amp; 0.036 &amp; -0.654 &amp; (0.257) &amp; [-0.094, 0.047] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; -0.092 &amp; 0.053 &amp; -1.745 &amp; (0.082) &amp; [-0.197, 0.012] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.036 &amp; 0.043 &amp; -0.822 &amp; (0.411) &amp; [-0.121, 0.05] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.016 &amp; 0.033 &amp; -0.489 &amp; (0.625) &amp; [-0.082, 0.049] \\ 
##   II. Punitive vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.007 &amp; 0.053 &amp; 0.139 &amp; (0.555) &amp; [-0.097, 0.112] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.019 &amp; 0.042 &amp; 0.456 &amp; (0.676) &amp; [-0.064, 0.103] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.018 &amp; 0.035 &amp; 0.515 &amp; (0.697) &amp; [-0.051, 0.087] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.05 &amp; 0.056 &amp; 0.893 &amp; (0.814) &amp; [-0.06, 0.16] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.068 &amp; 0.043 &amp; -1.568 &amp; (0.059) &amp; [-0.153, 0.017] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.025 &amp; 0.035 &amp; -0.72 &amp; (0.236) &amp; [-0.093, 0.043] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; 0.039 &amp; 0.05 &amp; 0.781 &amp; (0.435) &amp; [-0.06, 0.138] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.088 &amp; 0.042 &amp; -2.1 &amp; (0.036) &amp; [-0.171, -0.006] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.041 &amp; 0.032 &amp; -1.278 &amp; (0.202) &amp; [-0.105, 0.022] \\ 
##   III. Punitive vs. Monitoring &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.018 &amp; 0.058 &amp; 0.319 &amp; (0.75) &amp; [-0.095, 0.132] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.033 &amp; 0.049 &amp; 0.665 &amp; (0.506) &amp; [-0.064, 0.129] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.026 &amp; 0.037 &amp; 0.685 &amp; (0.494) &amp; [-0.048, 0.099] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.109 &amp; 0.058 &amp; 1.879 &amp; (0.061) &amp; [-0.005, 0.223] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.024 &amp; 0.045 &amp; -0.532 &amp; (0.595) &amp; [-0.111, 0.064] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.001 &amp; 0.036 &amp; -0.042 &amp; (0.966) &amp; [-0.071, 0.068] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; 0.132 &amp; 0.053 &amp; 2.48 &amp; (0.014) &amp; [0.027, 0.236] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.053 &amp; 0.043 &amp; -1.216 &amp; (0.224) &amp; [-0.137, 0.032] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.025 &amp; 0.033 &amp; -0.752 &amp; (0.452) &amp; [-0.09, 0.04] \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a11-p.a-29-predicted-means-differences-and-percent-differences-from-two-group-parametric-estimators" class="section level2">
<h2><span class="header-section-number">2.14</span> Table A11 (p. A-29): Predicted Means, Differences, and Percent Differences from Two-Group Parametric Estimators</h2>
<pre class="r"><code>ccdat &lt;- data.frame(TA1=0, TA2=0, block=1:17)
mcdat &lt;- data.frame(TA1=1, TA2=0, block=1:17)
pcdat &lt;- data.frame(TA1=0, TA2=1, block=1:17)

cmdat &lt;- data.frame(TA0=0, TA2=0, block=1:17)
pmdat &lt;- data.frame(TA0=0, TA2=1, block=1:17)
cmdat.i2 &lt;- data.frame(TA0=0, TA2=0, block=c(1:4,6:17))
pmdat.i2 &lt;- data.frame(TA0=0, TA2=1, block=c(1:4,6:17))

fit.wb.mc &lt;- fit.wb.pc &lt;- fit.wb.pm &lt;- fit.wh.mc &lt;- fit.wh.pc &lt;- fit.wh.pm &lt;- fit.bh.mc &lt;- fit.bh.pc &lt;- fit.bh.pm &lt;- list() # store results from ITT est w/ block FE (no other covs)

for(i in 1:length(outvars.wb)){  
  
  model.mc &lt;- paste(outvars.wb[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.wb.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.wb.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.wb.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)
}

for(i in 1:length(outvars.wh)){  
  
  model.mc &lt;- paste(outvars.wh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.wh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.wh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.wh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)
}

for(i in 1:length(outvars.bh)){  
  
  model.mc &lt;- paste(outvars.bh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.bh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1),], weights=ipw10)
  fit.bh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2),], weights=ipw20)
  fit.bh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2),], weights=ipw21)
}

pred.vals &lt;- list()

for(i in 1:4){
  
  #--------- m-c ---------#
  # wb
  mc.wb &lt;- c(mean(predict(fit.wb.mc[[i]], mcdat)), mean(predict(fit.wb.mc[[i]], ccdat)))
  # wh
  mc.wh &lt;- c(mean(predict(fit.wh.mc[[i]], mcdat)), mean(predict(fit.wh.mc[[i]], ccdat)))
  # bh
  mc.bh &lt;- c(mean(predict(fit.bh.mc[[i]], mcdat)), mean(predict(fit.bh.mc[[i]], ccdat)))
  
  #--------- p-c ---------#
  # wb
  pc.wb &lt;- c(mean(predict(fit.wb.pc[[i]], pcdat)), mean(predict(fit.wb.pc[[i]], ccdat)))
  # wh
  pc.wh &lt;- c(mean(predict(fit.wh.pc[[i]], pcdat)), mean(predict(fit.wh.pc[[i]], ccdat)))
  # bh
  pc.bh &lt;- c(mean(predict(fit.bh.pc[[i]], pcdat)), mean(predict(fit.bh.pc[[i]], ccdat)))
  
  #--------- p-m ---------#
  
  if(i != 2){
    # wb
    pm.wb &lt;- c(mean(predict(fit.wb.pm[[i]], pmdat)), mean(predict(fit.wb.pm[[i]], cmdat)))
    # w
    pm.wh &lt;- c(mean(predict(fit.wh.pm[[i]], pmdat)), mean(predict(fit.wh.pm[[i]], cmdat)))
    # bh
    pm.bh &lt;- c(mean(predict(fit.bh.pm[[i]], pmdat)), mean(predict(fit.bh.pm[[i]], cmdat)))
  } else {
    # wb
    pm.wb &lt;- c(mean(predict(fit.wb.pm[[i]], pmdat.i2)), mean(predict(fit.wb.pm[[i]], cmdat.i2)))
    # w
    pm.wh &lt;- c(mean(predict(fit.wh.pm[[i]], pmdat.i2)), mean(predict(fit.wh.pm[[i]], cmdat.i2)))
    # bh
    pm.bh &lt;- c(mean(predict(fit.bh.pm[[i]], pmdat.i2)), mean(predict(fit.bh.pm[[i]], cmdat.i2)))
    
  }
  
  pred.vals[[i]] &lt;- rbind(mc.wb, mc.wh, mc.bh,
                          pc.wb, pc.wh, pc.bh,
                          pm.wb, pm.wh, pm.bh)
  
}

names(pred.vals) &lt;- c(&quot;meet&quot;,&quot;index&quot;,&quot;callback&quot;,&quot;offer&quot;)

pred.mat &lt;- NULL

for(i in 1:nrow(pred.vals[[1]])){
  for(j in 2:length(pred.vals)){
    pred.mat &lt;- rbind(pred.mat, pred.vals[[j]][i,])
  }
}

pred.out &lt;- cbind(pred.mat, pred.mat[,1]-pred.mat[,2], (pred.mat[,1]-pred.mat[,2])/abs(pred.mat[,2])*100)
colnames(pred.out) &lt;- c(&quot;Predicted Treatment Mean&quot;, &quot;Predicted Control Mean&quot;, &quot;Difference&quot;, &quot;Percent Difference&quot;)
for(i in 1:ncol(pred.out)) pred.out[,i] &lt;- round(pred.out[,i], digits=3)

pred.out &lt;- cbind(Outcome=itt.2g.tab[,1], pred.out)

pred.out2 &lt;- list()
seclabs.index &lt;- 0

for(i in 1:length(start.rows)){
  if(i %in% c(1,4,7)){
    seclabs.index &lt;- seclabs.index + 1
    pred.out2[[i]] &lt;- rbind(c(seclabs[seclabs.index], rep(NA,4)),
                           c(sublabs[i], rep(NA,4)),
                           pred.out[start.rows[i]:stop.rows[i],])        
  } else {
    pred.out2[[i]] &lt;- rbind(c(sublabs[i], rep(NA,4)), pred.out[start.rows[i]:stop.rows[i],])    
  }
}

pred.out2 &lt;- do.call(rbind, pred.out2)

kable(pred.out2, caption=&quot;**Table A11**&quot;)</code></pre>
<table>
<caption><strong>Table A11</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Predicted Treatment Mean</th>
<th align="left">Predicted Control Mean</th>
<th align="left">Difference</th>
<th align="left">Percent Difference</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">I. Monitoring vs. Control</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">A. White vs. Black</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.023</td>
<td align="left">0.038</td>
<td align="left">-0.015</td>
<td align="left">-39.204</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.006</td>
<td align="left">0.007</td>
<td align="left">-0.002</td>
<td align="left">-21.372</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.005</td>
<td align="left">0.008</td>
<td align="left">-0.003</td>
<td align="left">-40.863</td>
</tr>
<tr class="even">
<td align="left">B. White vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">-0.095</td>
<td align="left">-0.034</td>
<td align="left">-0.061</td>
<td align="left">-178.801</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">0.001</td>
<td align="left">0.038</td>
<td align="left">-0.036</td>
<td align="left">-96.164</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.023</td>
<td align="left">0.04</td>
<td align="left">-0.017</td>
<td align="left">-42.45</td>
</tr>
<tr class="even">
<td align="left">C. Black vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">-0.162</td>
<td align="left">-0.073</td>
<td align="left">-0.089</td>
<td align="left">-121.727</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.004</td>
<td align="left">0.03</td>
<td align="left">-0.035</td>
<td align="left">-114.689</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">0.018</td>
<td align="left">0.032</td>
<td align="left">-0.014</td>
<td align="left">-42.853</td>
</tr>
<tr class="even">
<td align="left">II. Punitive vs. Control</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">A. White vs. Black</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.007</td>
<td align="left">0</td>
<td align="left">0.007</td>
<td align="left">7904.738</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.012</td>
<td align="left">-0.007</td>
<td align="left">0.019</td>
<td align="left">281.195</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.032</td>
<td align="left">0.014</td>
<td align="left">0.018</td>
<td align="left">127.36</td>
</tr>
<tr class="odd">
<td align="left">B. White vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.009</td>
<td align="left">-0.039</td>
<td align="left">0.048</td>
<td align="left">122.284</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.005</td>
<td align="left">0.061</td>
<td align="left">-0.066</td>
<td align="left">-108.818</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.013</td>
<td align="left">0.034</td>
<td align="left">-0.021</td>
<td align="left">-62.201</td>
</tr>
<tr class="odd">
<td align="left">C. Black vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">-0.012</td>
<td align="left">-0.051</td>
<td align="left">0.039</td>
<td align="left">76.051</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.018</td>
<td align="left">0.068</td>
<td align="left">-0.085</td>
<td align="left">-126.385</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.019</td>
<td align="left">0.02</td>
<td align="left">-0.039</td>
<td align="left">-196.921</td>
</tr>
<tr class="odd">
<td align="left">III. Punitive vs. Monitoring</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">A. White vs. Black</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.019</td>
<td align="left">0</td>
<td align="left">0.018</td>
<td align="left">6621.1</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.055</td>
<td align="left">0.022</td>
<td align="left">0.033</td>
<td align="left">147.447</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.018</td>
<td align="left">-0.008</td>
<td align="left">0.026</td>
<td align="left">322.614</td>
</tr>
<tr class="even">
<td align="left">B. White vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.014</td>
<td align="left">-0.092</td>
<td align="left">0.107</td>
<td align="left">115.589</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.02</td>
<td align="left">0</td>
<td align="left">-0.019</td>
<td align="left">-4046.942</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.002</td>
<td align="left">-0.004</td>
<td align="left">0.002</td>
<td align="left">49.995</td>
</tr>
<tr class="even">
<td align="left">C. Black vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">-0.008</td>
<td align="left">-0.147</td>
<td align="left">0.139</td>
<td align="left">94.696</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.074</td>
<td align="left">-0.023</td>
<td align="left">-0.052</td>
<td align="left">-229.774</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.019</td>
<td align="left">0.004</td>
<td align="left">-0.024</td>
<td align="left">-551.477</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 17:10:28 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
##   \hline
## Outcome &amp; Predicted Treatment Mean &amp; Predicted Control Mean &amp; Difference &amp; Percent Difference \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.023 &amp; 0.038 &amp; -0.015 &amp; -39.204 \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.006 &amp; 0.007 &amp; -0.002 &amp; -21.372 \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.005 &amp; 0.008 &amp; -0.003 &amp; -40.863 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; -0.095 &amp; -0.034 &amp; -0.061 &amp; -178.801 \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; 0.001 &amp; 0.038 &amp; -0.036 &amp; -96.164 \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; 0.023 &amp; 0.04 &amp; -0.017 &amp; -42.45 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; -0.162 &amp; -0.073 &amp; -0.089 &amp; -121.727 \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.004 &amp; 0.03 &amp; -0.035 &amp; -114.689 \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; 0.018 &amp; 0.032 &amp; -0.014 &amp; -42.853 \\ 
##   II. Punitive vs. Control &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.007 &amp; 0 &amp; 0.007 &amp; 7904.738 \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.012 &amp; -0.007 &amp; 0.019 &amp; 281.195 \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.032 &amp; 0.014 &amp; 0.018 &amp; 127.36 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.009 &amp; -0.039 &amp; 0.048 &amp; 122.284 \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.005 &amp; 0.061 &amp; -0.066 &amp; -108.818 \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; 0.013 &amp; 0.034 &amp; -0.021 &amp; -62.201 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; -0.012 &amp; -0.051 &amp; 0.039 &amp; 76.051 \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.018 &amp; 0.068 &amp; -0.085 &amp; -126.385 \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.019 &amp; 0.02 &amp; -0.039 &amp; -196.921 \\ 
##   III. Punitive vs. Monitoring &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.019 &amp; 0 &amp; 0.018 &amp; 6621.1 \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.055 &amp; 0.022 &amp; 0.033 &amp; 147.447 \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.018 &amp; -0.008 &amp; 0.026 &amp; 322.614 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.014 &amp; -0.092 &amp; 0.107 &amp; 115.589 \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.02 &amp; 0 &amp; -0.019 &amp; -4046.942 \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.002 &amp; -0.004 &amp; 0.002 &amp; 49.995 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; -0.008 &amp; -0.147 &amp; 0.139 &amp; 94.696 \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.074 &amp; -0.023 &amp; -0.052 &amp; -229.774 \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.019 &amp; 0.004 &amp; -0.024 &amp; -551.477 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a12-p.a-31-incidence-of-early-stage-discrimination-subjective-measures" class="section level2">
<h2><span class="header-section-number">2.15</span> Table A12 (p. A-31): Incidence of Early Stage Discrimination: Subjective Measures</h2>
<pre class="r"><code>table_a12 &lt;- es_discrim_full_table[-c(1,2,14,15,27,28),]

print(xtable(table_a12, caption=c(&quot;Incidence of Early Stage Discrimination: Subjective Measures&quot;,
                            &quot;Incidence of Early Stage Discrimination: Subjective Measure&quot;)),
      file=&quot;out_tablea12_early_stage_discrim_subjective.tex&quot;,
      include.rownames=FALSE)

table_a12_audit &lt;- es_discrim_full_table[-c(1,2,14,15,27,28),1:6]
table_a12_expsamp &lt;- es_discrim_full_table[-c(1,2,14,15,27,28),c(1, 7:11)]
table_a12_control &lt;- es_discrim_full_table[-c(1,2,14,15,27,28),c(1, 12:16)]

colnames(table_a12_audit)[2:6] &lt;- colnames(table_a12_expsamp)[2:6] &lt;- colnames(table_a12_control)[2:6] &lt;- c(&quot;Majority Group Mean&quot;, &quot;Minority Group Mean&quot;, &quot;Difference (Maj-Min)&quot;, &quot;p-value&quot;, &quot;[N]&quot;)

kable(table_a12_audit, caption=&quot;**Table A12, Panel I. All Pursued Cases in Audit Sample**&quot;)</code></pre>
<table>
<caption><strong>Table A12, Panel I. All Pursued Cases in Audit Sample</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">No. of attributes brought up (White vs. Black)</td>
<td align="left">1.101</td>
<td align="left">1.041</td>
<td align="left">0.06</td>
<td align="left">(0.136)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - skeptical response (White vs. Black)</td>
<td align="left">0.053</td>
<td align="left">0.027</td>
<td align="left">0.026</td>
<td align="left">(0.001)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - positive response (White vs. Black)</td>
<td align="left">0.132</td>
<td align="left">0.125</td>
<td align="left">0.007</td>
<td align="left">(0.637)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - neutral response (White vs. Black)</td>
<td align="left">0.918</td>
<td align="left">0.894</td>
<td align="left">0.024</td>
<td align="left">(0.501)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - negative response (White vs. Black)</td>
<td align="left">0.051</td>
<td align="left">0.022</td>
<td align="left">0.029</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - skeptical response (White vs. Black)</td>
<td align="left">0.012</td>
<td align="left">0.008</td>
<td align="left">0.005</td>
<td align="left">(0.021)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - positive response (White vs. Black)</td>
<td align="left">0.033</td>
<td align="left">0.033</td>
<td align="left">0</td>
<td align="left">(0.933)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - neutral response (White vs. Black)</td>
<td align="left">0.35</td>
<td align="left">0.328</td>
<td align="left">0.022</td>
<td align="left">(0.026)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - negative response (White vs. Black)</td>
<td align="left">0.013</td>
<td align="left">0.006</td>
<td align="left">0.007</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Responded skeptically for any attribute (White vs. Black)</td>
<td align="left">0.032</td>
<td align="left">0.016</td>
<td align="left">0.017</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Responded negatively for any attribute (White vs. Black)</td>
<td align="left">0.035</td>
<td align="left">0.016</td>
<td align="left">0.019</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. of attributes brought up (White vs. Hispanic)</td>
<td align="left">1.101</td>
<td align="left">1.07</td>
<td align="left">0.031</td>
<td align="left">(0.434)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - skeptical response (White vs. Hispanic)</td>
<td align="left">0.053</td>
<td align="left">0.065</td>
<td align="left">-0.012</td>
<td align="left">(0.241)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - positive response (White vs. Hispanic)</td>
<td align="left">0.132</td>
<td align="left">0.14</td>
<td align="left">-0.008</td>
<td align="left">(0.532)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - neutral response (White vs. Hispanic)</td>
<td align="left">0.918</td>
<td align="left">0.884</td>
<td align="left">0.035</td>
<td align="left">(0.32)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - negative response (White vs. Hispanic)</td>
<td align="left">0.051</td>
<td align="left">0.046</td>
<td align="left">0.005</td>
<td align="left">(0.543)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - skeptical response (White vs. Hispanic)</td>
<td align="left">0.012</td>
<td align="left">0.016</td>
<td align="left">-0.004</td>
<td align="left">(0.114)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - positive response (White vs. Hispanic)</td>
<td align="left">0.033</td>
<td align="left">0.037</td>
<td align="left">-0.004</td>
<td align="left">(0.27)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - neutral response (White vs. Hispanic)</td>
<td align="left">0.35</td>
<td align="left">0.34</td>
<td align="left">0.01</td>
<td align="left">(0.305)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - negative response (White vs. Hispanic)</td>
<td align="left">0.013</td>
<td align="left">0.013</td>
<td align="left">0</td>
<td align="left">(0.929)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Responded skeptically for any attribute (White vs. Hispanic)</td>
<td align="left">0.032</td>
<td align="left">0.036</td>
<td align="left">-0.004</td>
<td align="left">(0.435)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Responded negatively for any attribute (White vs. Hispanic)</td>
<td align="left">0.035</td>
<td align="left">0.03</td>
<td align="left">0.004</td>
<td align="left">(0.324)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. of attributes brought up (Black vs. Hispanic)</td>
<td align="left">1.041</td>
<td align="left">1.07</td>
<td align="left">-0.029</td>
<td align="left">(0.47)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - skeptical response (Black vs. Hispanic)</td>
<td align="left">0.027</td>
<td align="left">0.065</td>
<td align="left">-0.038</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - positive response (Black vs. Hispanic)</td>
<td align="left">0.125</td>
<td align="left">0.14</td>
<td align="left">-0.015</td>
<td align="left">(0.327)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - neutral response (Black vs. Hispanic)</td>
<td align="left">0.894</td>
<td align="left">0.884</td>
<td align="left">0.01</td>
<td align="left">(0.775)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - negative response (Black vs. Hispanic)</td>
<td align="left">0.022</td>
<td align="left">0.046</td>
<td align="left">-0.024</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - skeptical response (Black vs. Hispanic)</td>
<td align="left">0.008</td>
<td align="left">0.016</td>
<td align="left">-0.008</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - positive response (Black vs. Hispanic)</td>
<td align="left">0.033</td>
<td align="left">0.037</td>
<td align="left">-0.004</td>
<td align="left">(0.278)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - neutral response (Black vs. Hispanic)</td>
<td align="left">0.328</td>
<td align="left">0.34</td>
<td align="left">-0.013</td>
<td align="left">(0.202)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - negative response (Black vs. Hispanic)</td>
<td align="left">0.006</td>
<td align="left">0.013</td>
<td align="left">-0.007</td>
<td align="left">(0.001)</td>
<td align="left">[2711]</td>
</tr>
<tr class="even">
<td align="left">Responded skeptically for any attribute (Black vs. Hispanic)</td>
<td align="left">0.016</td>
<td align="left">0.036</td>
<td align="left">-0.02</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
<tr class="odd">
<td align="left">Responded negatively for any attribute (Black vs. Hispanic)</td>
<td align="left">0.016</td>
<td align="left">0.03</td>
<td align="left">-0.014</td>
<td align="left">(0)</td>
<td align="left">[2711]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(table_a12_expsamp, caption=&quot;**Table A12, Panel II. All Pursued Cases in Experimental Sample**&quot;)</code></pre>
<table>
<caption><strong>Table A12, Panel II. All Pursued Cases in Experimental Sample</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">No. of attributes brought up (White vs. Black)</td>
<td align="left">2.023</td>
<td align="left">1.936</td>
<td align="left">0.087</td>
<td align="left">(0.334)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - skeptical response (White vs. Black)</td>
<td align="left">0.077</td>
<td align="left">0.041</td>
<td align="left">0.035</td>
<td align="left">(0.073)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - positive response (White vs. Black)</td>
<td align="left">0.262</td>
<td align="left">0.256</td>
<td align="left">0.006</td>
<td align="left">(0.876)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - neutral response (White vs. Black)</td>
<td align="left">1.703</td>
<td align="left">1.668</td>
<td align="left">0.035</td>
<td align="left">(0.675)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - negative response (White vs. Black)</td>
<td align="left">0.058</td>
<td align="left">0.012</td>
<td align="left">0.046</td>
<td align="left">(0.001)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - skeptical response (White vs. Black)</td>
<td align="left">0.018</td>
<td align="left">0.015</td>
<td align="left">0.003</td>
<td align="left">(0.602)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - positive response (White vs. Black)</td>
<td align="left">0.07</td>
<td align="left">0.07</td>
<td align="left">0.001</td>
<td align="left">(0.925)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - neutral response (White vs. Black)</td>
<td align="left">0.706</td>
<td align="left">0.646</td>
<td align="left">0.06</td>
<td align="left">(0.011)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - negative response (White vs. Black)</td>
<td align="left">0.014</td>
<td align="left">0.003</td>
<td align="left">0.011</td>
<td align="left">(0.002)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Responded skeptically for any attribute (White vs. Black)</td>
<td align="left">0.049</td>
<td align="left">0.025</td>
<td align="left">0.025</td>
<td align="left">(0.018)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Responded negatively for any attribute (White vs. Black)</td>
<td align="left">0.04</td>
<td align="left">0.009</td>
<td align="left">0.031</td>
<td align="left">(0)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. of attributes brought up (White vs. Hispanic)</td>
<td align="left">2.023</td>
<td align="left">2.072</td>
<td align="left">-0.049</td>
<td align="left">(0.549)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - skeptical response (White vs. Hispanic)</td>
<td align="left">0.077</td>
<td align="left">0.081</td>
<td align="left">-0.005</td>
<td align="left">(0.831)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - positive response (White vs. Hispanic)</td>
<td align="left">0.262</td>
<td align="left">0.27</td>
<td align="left">-0.008</td>
<td align="left">(0.832)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - neutral response (White vs. Hispanic)</td>
<td align="left">1.703</td>
<td align="left">1.75</td>
<td align="left">-0.047</td>
<td align="left">(0.524)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - negative response (White vs. Hispanic)</td>
<td align="left">0.058</td>
<td align="left">0.052</td>
<td align="left">0.006</td>
<td align="left">(0.706)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - skeptical response (White vs. Hispanic)</td>
<td align="left">0.018</td>
<td align="left">0.023</td>
<td align="left">-0.005</td>
<td align="left">(0.39)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - positive response (White vs. Hispanic)</td>
<td align="left">0.07</td>
<td align="left">0.077</td>
<td align="left">-0.007</td>
<td align="left">(0.514)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - neutral response (White vs. Hispanic)</td>
<td align="left">0.706</td>
<td align="left">0.714</td>
<td align="left">-0.009</td>
<td align="left">(0.656)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - negative response (White vs. Hispanic)</td>
<td align="left">0.014</td>
<td align="left">0.015</td>
<td align="left">-0.002</td>
<td align="left">(0.742)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Responded skeptically for any attribute (White vs. Hispanic)</td>
<td align="left">0.049</td>
<td align="left">0.051</td>
<td align="left">-0.002</td>
<td align="left">(0.898)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Responded negatively for any attribute (White vs. Hispanic)</td>
<td align="left">0.04</td>
<td align="left">0.038</td>
<td align="left">0.002</td>
<td align="left">(0.876)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. of attributes brought up (Black vs. Hispanic)</td>
<td align="left">1.936</td>
<td align="left">2.072</td>
<td align="left">-0.136</td>
<td align="left">(0.107)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - skeptical response (Black vs. Hispanic)</td>
<td align="left">0.041</td>
<td align="left">0.081</td>
<td align="left">-0.04</td>
<td align="left">(0.045)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - positive response (Black vs. Hispanic)</td>
<td align="left">0.256</td>
<td align="left">0.27</td>
<td align="left">-0.014</td>
<td align="left">(0.749)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - neutral response (Black vs. Hispanic)</td>
<td align="left">1.668</td>
<td align="left">1.75</td>
<td align="left">-0.083</td>
<td align="left">(0.298)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - negative response (Black vs. Hispanic)</td>
<td align="left">0.012</td>
<td align="left">0.052</td>
<td align="left">-0.04</td>
<td align="left">(0.002)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - skeptical response (Black vs. Hispanic)</td>
<td align="left">0.015</td>
<td align="left">0.023</td>
<td align="left">-0.008</td>
<td align="left">(0.196)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - positive response (Black vs. Hispanic)</td>
<td align="left">0.07</td>
<td align="left">0.077</td>
<td align="left">-0.008</td>
<td align="left">(0.498)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - neutral response (Black vs. Hispanic)</td>
<td align="left">0.646</td>
<td align="left">0.714</td>
<td align="left">-0.069</td>
<td align="left">(0.003)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - negative response (Black vs. Hispanic)</td>
<td align="left">0.003</td>
<td align="left">0.015</td>
<td align="left">-0.012</td>
<td align="left">(0.001)</td>
<td align="left">[653]</td>
</tr>
<tr class="even">
<td align="left">Responded skeptically for any attribute (Black vs. Hispanic)</td>
<td align="left">0.025</td>
<td align="left">0.051</td>
<td align="left">-0.026</td>
<td align="left">(0.011)</td>
<td align="left">[653]</td>
</tr>
<tr class="odd">
<td align="left">Responded negatively for any attribute (Black vs. Hispanic)</td>
<td align="left">0.009</td>
<td align="left">0.038</td>
<td align="left">-0.029</td>
<td align="left">(0)</td>
<td align="left">[653]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(table_a12_control, caption=&quot;**Table A12, Panel III. All Pursued Cases in Control Group**&quot;)</code></pre>
<table>
<caption><strong>Table A12, Panel III. All Pursued Cases in Control Group</strong></caption>
<thead>
<tr class="header">
<th align="left">Measure</th>
<th align="left">Majority Group Mean</th>
<th align="left">Minority Group Mean</th>
<th align="left">Difference (Maj-Min)</th>
<th align="left">p-value</th>
<th align="left">[N]</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">No. of attributes brought up (White vs. Black)</td>
<td align="left">2.151</td>
<td align="left">1.961</td>
<td align="left">0.19</td>
<td align="left">(0.145)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - skeptical response (White vs. Black)</td>
<td align="left">0.097</td>
<td align="left">0.039</td>
<td align="left">0.057</td>
<td align="left">(0.106)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - positive response (White vs. Black)</td>
<td align="left">0.269</td>
<td align="left">0.258</td>
<td align="left">0.011</td>
<td align="left">(0.852)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - neutral response (White vs. Black)</td>
<td align="left">1.821</td>
<td align="left">1.685</td>
<td align="left">0.136</td>
<td align="left">(0.259)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - negative response (White vs. Black)</td>
<td align="left">0.061</td>
<td align="left">0.018</td>
<td align="left">0.043</td>
<td align="left">(0.064)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - skeptical response (White vs. Black)</td>
<td align="left">0.022</td>
<td align="left">0.011</td>
<td align="left">0.012</td>
<td align="left">(0.176)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - positive response (White vs. Black)</td>
<td align="left">0.071</td>
<td align="left">0.066</td>
<td align="left">0.005</td>
<td align="left">(0.743)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - neutral response (White vs. Black)</td>
<td align="left">0.745</td>
<td align="left">0.666</td>
<td align="left">0.078</td>
<td align="left">(0.028)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - negative response (White vs. Black)</td>
<td align="left">0.016</td>
<td align="left">0.003</td>
<td align="left">0.013</td>
<td align="left">(0.027)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Responded skeptically for any attribute (White vs. Black)</td>
<td align="left">0.054</td>
<td align="left">0.018</td>
<td align="left">0.036</td>
<td align="left">(0.025)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Responded negatively for any attribute (White vs. Black)</td>
<td align="left">0.039</td>
<td align="left">0.011</td>
<td align="left">0.029</td>
<td align="left">(0.032)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. of attributes brought up (White vs. Hispanic)</td>
<td align="left">2.151</td>
<td align="left">1.989</td>
<td align="left">0.161</td>
<td align="left">(0.19)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - skeptical response (White vs. Hispanic)</td>
<td align="left">0.097</td>
<td align="left">0.039</td>
<td align="left">0.057</td>
<td align="left">(0.074)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - positive response (White vs. Hispanic)</td>
<td align="left">0.269</td>
<td align="left">0.24</td>
<td align="left">0.029</td>
<td align="left">(0.533)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - neutral response (White vs. Hispanic)</td>
<td align="left">1.821</td>
<td align="left">1.728</td>
<td align="left">0.093</td>
<td align="left">(0.383)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - negative response (White vs. Hispanic)</td>
<td align="left">0.061</td>
<td align="left">0.022</td>
<td align="left">0.039</td>
<td align="left">(0.07)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - skeptical response (White vs. Hispanic)</td>
<td align="left">0.022</td>
<td align="left">0.012</td>
<td align="left">0.01</td>
<td align="left">(0.218)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - positive response (White vs. Hispanic)</td>
<td align="left">0.071</td>
<td align="left">0.069</td>
<td align="left">0.002</td>
<td align="left">(0.898)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - neutral response (White vs. Hispanic)</td>
<td align="left">0.745</td>
<td align="left">0.733</td>
<td align="left">0.011</td>
<td align="left">(0.699)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - negative response (White vs. Hispanic)</td>
<td align="left">0.016</td>
<td align="left">0.007</td>
<td align="left">0.009</td>
<td align="left">(0.204)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Responded skeptically for any attribute (White vs. Hispanic)</td>
<td align="left">0.054</td>
<td align="left">0.029</td>
<td align="left">0.025</td>
<td align="left">(0.145)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Responded negatively for any attribute (White vs. Hispanic)</td>
<td align="left">0.039</td>
<td align="left">0.018</td>
<td align="left">0.022</td>
<td align="left">(0.109)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. of attributes brought up (Black vs. Hispanic)</td>
<td align="left">1.961</td>
<td align="left">1.989</td>
<td align="left">-0.029</td>
<td align="left">(0.813)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - skeptical response (Black vs. Hispanic)</td>
<td align="left">0.039</td>
<td align="left">0.039</td>
<td align="left">0</td>
<td align="left">(1)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - positive response (Black vs. Hispanic)</td>
<td align="left">0.258</td>
<td align="left">0.24</td>
<td align="left">0.018</td>
<td align="left">(0.772)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">No. attributes - neutral response (Black vs. Hispanic)</td>
<td align="left">1.685</td>
<td align="left">1.728</td>
<td align="left">-0.043</td>
<td align="left">(0.703)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">No. attributes - negative response (Black vs. Hispanic)</td>
<td align="left">0.018</td>
<td align="left">0.022</td>
<td align="left">-0.004</td>
<td align="left">(0.782)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - skeptical response (Black vs. Hispanic)</td>
<td align="left">0.011</td>
<td align="left">0.012</td>
<td align="left">-0.001</td>
<td align="left">(0.849)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - positive response (Black vs. Hispanic)</td>
<td align="left">0.066</td>
<td align="left">0.069</td>
<td align="left">-0.003</td>
<td align="left">(0.831)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Pct. of attributes - neutral response (Black vs. Hispanic)</td>
<td align="left">0.666</td>
<td align="left">0.733</td>
<td align="left">-0.067</td>
<td align="left">(0.036)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Pct. of attributes - negative response (Black vs. Hispanic)</td>
<td align="left">0.003</td>
<td align="left">0.007</td>
<td align="left">-0.005</td>
<td align="left">(0.302)</td>
<td align="left">[279]</td>
</tr>
<tr class="even">
<td align="left">Responded skeptically for any attribute (Black vs. Hispanic)</td>
<td align="left">0.018</td>
<td align="left">0.029</td>
<td align="left">-0.011</td>
<td align="left">(0.367)</td>
<td align="left">[279]</td>
</tr>
<tr class="odd">
<td align="left">Responded negatively for any attribute (Black vs. Hispanic)</td>
<td align="left">0.011</td>
<td align="left">0.018</td>
<td align="left">-0.007</td>
<td align="left">(0.415)</td>
<td align="left">[279]</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a13-p.a-32-treatment-noncompliance-incidence" class="section level2">
<h2><span class="header-section-number">2.16</span> Table A13 (p. A-32): Treatment Noncompliance Incidence</h2>
<pre class="r"><code>nc.counts &lt;- table(dat$TA, dat$TD, useNA=&quot;ifany&quot;)
nc.props &lt;- table(dat$TA, dat$TD, useNA=&quot;ifany&quot;)/rowSums(table(dat$TA, dat$TD, useNA=&quot;ifany&quot;))

table_a13 &lt;- cbind(nc.counts[,1], nc.props[,1],
              nc.counts[,2], nc.props[,2],
              nc.counts[,3], nc.props[,3],
              rowSums(table(dat$TA, dat$TD, useNA=&quot;ifany&quot;)))

table_a13[,c(2,4,6)] &lt;- round(table_a13[,c(2,4,6)], digits=2)
table_a13 &lt;- cbind(c(&quot;Control&quot;,&quot;Monitoring&quot;,&quot;Punitive&quot;), table_a13)
colnames(table_a13) &lt;- c(&quot;Assigned Arm&quot;,
                        &quot;Control (N)&quot;, &quot;Control (Proportion)&quot;, 
                        &quot;Monitoring (N)&quot;, &quot;Monitoring (Proportion)&quot;,
                        &quot;Punitive (N)&quot;, &quot;Punitive (Proportion)&quot;, &quot;Row Totals&quot;)

print(xtable(table_a13), file=&quot;out_tablea13_noncompliance_incidence.tex&quot;, include.rownames=FALSE)
rownames(table_a13) &lt;- NULL
kable(table_a13, caption=&quot;**Table A13**&quot;)</code></pre>
<table>
<caption><strong>Table A13</strong></caption>
<thead>
<tr class="header">
<th align="left">Assigned Arm</th>
<th align="left">Control (N)</th>
<th align="left">Control (Proportion)</th>
<th align="left">Monitoring (N)</th>
<th align="left">Monitoring (Proportion)</th>
<th align="left">Punitive (N)</th>
<th align="left">Punitive (Proportion)</th>
<th align="left">Row Totals</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Control</td>
<td align="left">279</td>
<td align="left">1</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">279</td>
</tr>
<tr class="even">
<td align="left">Monitoring</td>
<td align="left">31</td>
<td align="left">0.18</td>
<td align="left">143</td>
<td align="left">0.82</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">174</td>
</tr>
<tr class="odd">
<td align="left">Punitive</td>
<td align="left">38</td>
<td align="left">0.19</td>
<td align="left">17</td>
<td align="left">0.08</td>
<td align="left">145</td>
<td align="left">0.72</td>
<td align="left">200</td>
</tr>
</tbody>
</table>
</div>
<div id="table-a14-p.a-34-estimated-complier-average-causal-effects-of-messages-on-net-discrimination-levels" class="section level2">
<h2><span class="header-section-number">2.17</span> Table A14 (p. A-34): Estimated Complier Average Causal Effects of Messages on Net Discrimination Levels</h2>
<pre class="r"><code># DEFINE PAIRS OF TREATMENT-COMPARISON DIFFERENCES OF INTEREST
diffs &lt;- rbind(c(1,0),
               c(2,0),
               c(2,1))
colnames(diffs) &lt;- c(&quot;treatment&quot;,&quot;comparison&quot;)

# ---------------------------------------------------------------------- #
# Code treatment receipt two ways for P vs C comparison
# Alt Approach A: treat Z=punitive D=monitoring as fail-to-treat
# Alt Approach B: treat Z=punitive D=monitoring as effectively punitive messaging
dat$TD_a &lt;- dat$TD_b &lt;- dat$TD
dat$TD_a &lt;- with(dat, ifelse(TA==2 &amp; TD==1, 0, TD_a))
dat$TD_b &lt;- with(dat, ifelse(TA==2 &amp; TD==1, 2, TD_b))

# ---------------------------------------------------------------------- #
# CACE helper function
# inputs:
#  -- dat: data frame
#  -- Y: name of outcome variable
#  -- D: name of treatment receipt variable
#  -- trt_t: value of treatment arm (e.g., 2 for punitive)
#  -- trt_c: value of comparison arm (e.g., 0 for control)

cace.est &lt;- function(dat, Y, D, trt_t, trt_c){
  sub &lt;- dat[dat$TA %in% c(trt_t, trt_c), ]

  sub$Z &lt;- ifelse(sub$TA==trt_t, 1, 0)
  sub$D &lt;- ifelse(sub[[D]]==trt_t, 1, 0)

  sub$wtvar &lt;- sub[[paste0(&quot;ipw&quot;,trt_t,trt_c)]]
  
  itt.model &lt;- paste(Y, &quot;~ Z + as.factor(block)&quot;)
  ittd.model &lt;- &quot;D ~ Z + as.factor(block)&quot;
  iv.model &lt;- paste(Y, &quot;~ D + as.factor(block) | Z + as.factor(block)&quot;)
  
  itt.fit &lt;- lm(formula=itt.model, data=sub, weights=wtvar)
  ittd.fit &lt;- lm(formula=ittd.model, data=sub, weights=wtvar)
  iv.fit &lt;- ivreg(formula=iv.model, data=sub, weights=wtvar)
  
  itt.est &lt;- summary(itt.fit)$coef[2,1] # itt
  ittd.est &lt;- summary(ittd.fit)$coef[2,1] # itt.d
  cace.est &lt;- summary(iv.fit)$coef[2,1] # cace
  
  # p value: use one sided if M/C or P/C (j %in% 1:2), use two sided if P/M (j==3)
  if( trt_c == 0 ){
    cace.pval &lt;- pt(coef(summary(iv.fit))[,3], summary(iv.fit)$df[2], lower=TRUE)[2]
  } else {
    cace.pval &lt;- summary(iv.fit)$coef[2,4]
  }
  
  out &lt;- c(Y, trt_t, trt_c, round(itt.est,3), round(ittd.est,3), round(cace.est,3), round(cace.pval,3))
  names(out) &lt;- c(&quot;Y&quot;, &quot;treatment&quot;, &quot;comparison&quot;, &quot;ITT&quot;, &quot;ITT_D&quot;, &quot;CACE&quot;, &quot;p-value&quot;)
  return(out)
}

# M vs C - estimate CACE using IV
mc.cace.wb &lt;- rbind(cace.est(dat=dat, Y=outvars.wb[2], D=&quot;TD&quot;, trt_t=1, trt_c=0),
                    cace.est(dat=dat, Y=outvars.wb[3], D=&quot;TD&quot;, trt_t=1, trt_c=0),
                    cace.est(dat=dat, Y=outvars.wb[4], D=&quot;TD&quot;, trt_t=1, trt_c=0))

mc.cace.wh &lt;- rbind(cace.est(dat=dat, Y=outvars.wh[2], D=&quot;TD&quot;, trt_t=1, trt_c=0),
                    cace.est(dat=dat, Y=outvars.wh[3], D=&quot;TD&quot;, trt_t=1, trt_c=0),
                    cace.est(dat=dat, Y=outvars.wh[4], D=&quot;TD&quot;, trt_t=1, trt_c=0))

mc.cace.bh &lt;- rbind(cace.est(dat=dat, Y=outvars.bh[2], D=&quot;TD&quot;, trt_t=1, trt_c=0),
                    cace.est(dat=dat, Y=outvars.bh[3], D=&quot;TD&quot;, trt_t=1, trt_c=0),
                    cace.est(dat=dat, Y=outvars.bh[4], D=&quot;TD&quot;, trt_t=1, trt_c=0))

# P vs C (v1) - treat Z=punitive D=monitoring as fail-to-treat
pc1.cace.wb &lt;- rbind(cace.est(dat=dat, Y=outvars.wb[2], D=&quot;TD_a&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wb[3], D=&quot;TD_a&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wb[4], D=&quot;TD_a&quot;, trt_t=2, trt_c=0))

pc1.cace.wh &lt;- rbind(cace.est(dat=dat, Y=outvars.wh[2], D=&quot;TD_a&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wh[3], D=&quot;TD_a&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wh[4], D=&quot;TD_a&quot;, trt_t=2, trt_c=0))

pc1.cace.bh &lt;- rbind(cace.est(dat=dat, Y=outvars.bh[2], D=&quot;TD_a&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.bh[3], D=&quot;TD_a&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.bh[4], D=&quot;TD_a&quot;, trt_t=2, trt_c=0))

# P vs C (v2) - treat Z=punitive D=monitoring as effectively punitive messaging
pc2.cace.wb &lt;- rbind(cace.est(dat=dat, Y=outvars.wb[2], D=&quot;TD_b&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wb[3], D=&quot;TD_b&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wb[4], D=&quot;TD_b&quot;, trt_t=2, trt_c=0))

pc2.cace.wh &lt;- rbind(cace.est(dat=dat, Y=outvars.wh[2], D=&quot;TD_b&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wh[3], D=&quot;TD_b&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.wh[4], D=&quot;TD_b&quot;, trt_t=2, trt_c=0))

pc2.cace.bh &lt;- rbind(cace.est(dat=dat, Y=outvars.bh[2], D=&quot;TD_b&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.bh[3], D=&quot;TD_b&quot;, trt_t=2, trt_c=0),
                     cace.est(dat=dat, Y=outvars.bh[4], D=&quot;TD_b&quot;, trt_t=2, trt_c=0))

# Stitch together results
stitch.cace &lt;- function(wb, wh, bh, wb.labs, wh.labs, bh.labs){
  blanks &lt;- rep(NA, 4)
  out &lt;- rbind(blanks, wb[,4:7],
               blanks, wh[,4:7],
               blanks, bh[,4:7])
  out &lt;- cbind(c(&quot;A. White vs. Black&quot;, wb.labs[2:4],
                 &quot;B. White vs. Hispanic&quot;, wh.labs[2:4],
                 &quot;C. Black vs. Hispanic&quot;, bh.labs[2:4]), out)
  out[,1] &lt;- gsub(&quot;(White vs. Black)&quot;, &quot;&quot;, out[,1], fixed=TRUE)
  out[,1] &lt;- gsub(&quot;(White vs. Hispanic)&quot;, &quot;&quot;, out[,1], fixed=TRUE)
  out[,1] &lt;- gsub(&quot;(Black vs. Hispanic)&quot;, &quot;&quot;, out[,1], fixed=TRUE)

  rownames(out) &lt;- NULL
  colnames(out) &lt;- c(&quot;Outcome&quot;, &quot;ITT&quot;, &quot;ITT_D&quot;, &quot;CACE&quot;, &quot;p-value&quot;)
  return(out)
}

cace.mc &lt;- stitch.cace(wb=mc.cace.wb,
                       wh=mc.cace.wh,
                       bh=mc.cace.bh,
                       wb.labs=outvars.wb.labs,
                       wh.labs=outvars.wh.labs,
                       bh.labs=outvars.bh.labs)
cace.pc1 &lt;- stitch.cace(wb=pc1.cace.wb,
                        wh=pc1.cace.wh,
                        bh=pc1.cace.bh,
                        wb.labs=outvars.wb.labs,
                        wh.labs=outvars.wh.labs,
                        bh.labs=outvars.bh.labs)
cace.pc2 &lt;- stitch.cace(wb=pc2.cace.wb,
                        wh=pc2.cace.wh,
                        bh=pc2.cace.bh,
                        wb.labs=outvars.wb.labs,
                        wh.labs=outvars.wh.labs,
                        bh.labs=outvars.bh.labs)

# Assemble final CACE table (same info; two possible formats)
cace.table1 &lt;- rbind(c(&quot;I. Monitoring vs. Control&quot;, rep(NA, 4)),
                     cace.mc,
                     c(&quot;II. Punitive vs. Control (Upper Bound)&quot;, rep(NA,4)),
                     cace.pc1,
                     c(&quot;III. Punitive vs. Control (Lower Bound)&quot;, rep(NA, 4)),
                     cace.pc2)
cace.pc &lt;- list()
keep.rows &lt;- c(1,5,9)
for(i in 1:nrow(cace.pc1)){
  if(i %in% keep.rows){
    cace.pc[[i]] &lt;- cace.pc1[i,]
  } else {
    cace.pc[[i]] &lt;- rbind(c(cace.pc1[i,1], rep(NA, 4)),
                          c(&quot;v1&quot;,cace.pc1[i,2:5]),
                          c(&quot;v2&quot;,cace.pc2[i,2:5]))
  }
}
cace.pc &lt;- do.call(rbind, cace.pc)
cace.pc[,1] &lt;- gsub(&quot;v1&quot;, &quot;Upper bound&quot;, cace.pc[,1], fixed=TRUE)
cace.pc[,1] &lt;- gsub(&quot;v2&quot;, &quot;Lower bound&quot;, cace.pc[,1], fixed=TRUE)
cace.table2 &lt;- rbind(c(&quot;I. Monitoring vs. Control&quot;, rep(NA, 4)),
                     cace.mc,
                     c(&quot;II. Punitive vs. Control (Upper and Lower Bounds)&quot;, rep(NA, 4)),
                     cace.pc)
kable(cace.table2, caption=&quot;**Table A14**&quot;)</code></pre>
<table>
<caption><strong>Table A14</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">ITT</th>
<th align="left">ITT_D</th>
<th align="left">CACE</th>
<th align="left">p-value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">I. Monitoring vs. Control</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">A. White vs. Black</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="left">-0.015</td>
<td align="left">0.81</td>
<td align="left">-0.018</td>
<td align="left">0.388</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="left">-0.002</td>
<td align="left">0.81</td>
<td align="left">-0.002</td>
<td align="left">0.486</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit</td>
<td align="left">-0.003</td>
<td align="left">0.81</td>
<td align="left">-0.004</td>
<td align="left">0.464</td>
</tr>
<tr class="even">
<td align="left">B. White vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="left">-0.061</td>
<td align="left">0.81</td>
<td align="left">-0.075</td>
<td align="left">0.14</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="left">-0.036</td>
<td align="left">0.81</td>
<td align="left">-0.045</td>
<td align="left">0.202</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit</td>
<td align="left">-0.017</td>
<td align="left">0.81</td>
<td align="left">-0.021</td>
<td align="left">0.31</td>
</tr>
<tr class="even">
<td align="left">C. Black vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="left">-0.089</td>
<td align="left">0.81</td>
<td align="left">-0.106</td>
<td align="left">0.045</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="left">-0.035</td>
<td align="left">0.81</td>
<td align="left">-0.043</td>
<td align="left">0.206</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit</td>
<td align="left">-0.014</td>
<td align="left">0.81</td>
<td align="left">-0.017</td>
<td align="left">0.328</td>
</tr>
<tr class="even">
<td align="left">II. Punitive vs. Control (Upper and Lower Bounds)</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">A. White vs. Black</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Upper bound</td>
<td align="left">0.007</td>
<td align="left">0.718</td>
<td align="left">0.01</td>
<td align="left">0.555</td>
</tr>
<tr class="even">
<td align="left">Lower bound</td>
<td align="left">0.007</td>
<td align="left">0.8</td>
<td align="left">0.009</td>
<td align="left">0.555</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Upper bound</td>
<td align="left">0.019</td>
<td align="left">0.718</td>
<td align="left">0.027</td>
<td align="left">0.675</td>
</tr>
<tr class="odd">
<td align="left">Lower bound</td>
<td align="left">0.019</td>
<td align="left">0.8</td>
<td align="left">0.024</td>
<td align="left">0.676</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Upper bound</td>
<td align="left">0.018</td>
<td align="left">0.718</td>
<td align="left">0.025</td>
<td align="left">0.696</td>
</tr>
<tr class="even">
<td align="left">Lower bound</td>
<td align="left">0.018</td>
<td align="left">0.8</td>
<td align="left">0.023</td>
<td align="left">0.696</td>
</tr>
<tr class="odd">
<td align="left">B. White vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Upper bound</td>
<td align="left">0.048</td>
<td align="left">0.718</td>
<td align="left">0.067</td>
<td align="left">0.806</td>
</tr>
<tr class="even">
<td align="left">Lower bound</td>
<td align="left">0.048</td>
<td align="left">0.8</td>
<td align="left">0.061</td>
<td align="left">0.806</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Upper bound</td>
<td align="left">-0.066</td>
<td align="left">0.718</td>
<td align="left">-0.092</td>
<td align="left">0.056</td>
</tr>
<tr class="odd">
<td align="left">Lower bound</td>
<td align="left">-0.066</td>
<td align="left">0.8</td>
<td align="left">-0.083</td>
<td align="left">0.056</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Upper bound</td>
<td align="left">-0.021</td>
<td align="left">0.718</td>
<td align="left">-0.029</td>
<td align="left">0.269</td>
</tr>
<tr class="even">
<td align="left">Lower bound</td>
<td align="left">-0.021</td>
<td align="left">0.8</td>
<td align="left">-0.026</td>
<td align="left">0.269</td>
</tr>
<tr class="odd">
<td align="left">C. Black vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Upper bound</td>
<td align="left">0.039</td>
<td align="left">0.718</td>
<td align="left">0.053</td>
<td align="left">0.785</td>
</tr>
<tr class="even">
<td align="left">Lower bound</td>
<td align="left">0.039</td>
<td align="left">0.8</td>
<td align="left">0.047</td>
<td align="left">0.785</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Upper bound</td>
<td align="left">-0.085</td>
<td align="left">0.718</td>
<td align="left">-0.119</td>
<td align="left">0.02</td>
</tr>
<tr class="odd">
<td align="left">Lower bound</td>
<td align="left">-0.085</td>
<td align="left">0.8</td>
<td align="left">-0.107</td>
<td align="left">0.019</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Upper bound</td>
<td align="left">-0.039</td>
<td align="left">0.718</td>
<td align="left">-0.055</td>
<td align="left">0.123</td>
</tr>
<tr class="even">
<td align="left">Lower bound</td>
<td align="left">-0.039</td>
<td align="left">0.8</td>
<td align="left">-0.049</td>
<td align="left">0.122</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 17:10:30 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
##   \hline
## Outcome &amp; ITT &amp; ITT\_D &amp; CACE &amp; p-value \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; -0.015 &amp; 0.81 &amp; -0.018 &amp; 0.388 \\ 
##   Received post-visit callback  &amp; -0.002 &amp; 0.81 &amp; -0.002 &amp; 0.486 \\ 
##   Received post-visit offer for unit  &amp; -0.003 &amp; 0.81 &amp; -0.004 &amp; 0.464 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; -0.061 &amp; 0.81 &amp; -0.075 &amp; 0.14 \\ 
##   Received post-visit callback  &amp; -0.036 &amp; 0.81 &amp; -0.045 &amp; 0.202 \\ 
##   Received post-visit offer for unit  &amp; -0.017 &amp; 0.81 &amp; -0.021 &amp; 0.31 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; -0.089 &amp; 0.81 &amp; -0.106 &amp; 0.045 \\ 
##   Received post-visit callback  &amp; -0.035 &amp; 0.81 &amp; -0.043 &amp; 0.206 \\ 
##   Received post-visit offer for unit  &amp; -0.014 &amp; 0.81 &amp; -0.017 &amp; 0.328 \\ 
##   II. Punitive vs. Control (Upper Bound) &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; 0.007 &amp; 0.718 &amp; 0.01 &amp; 0.555 \\ 
##   Received post-visit callback  &amp; 0.019 &amp; 0.718 &amp; 0.027 &amp; 0.675 \\ 
##   Received post-visit offer for unit  &amp; 0.018 &amp; 0.718 &amp; 0.025 &amp; 0.696 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; 0.048 &amp; 0.718 &amp; 0.067 &amp; 0.806 \\ 
##   Received post-visit callback  &amp; -0.066 &amp; 0.718 &amp; -0.092 &amp; 0.056 \\ 
##   Received post-visit offer for unit  &amp; -0.021 &amp; 0.718 &amp; -0.029 &amp; 0.269 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; 0.039 &amp; 0.718 &amp; 0.053 &amp; 0.785 \\ 
##   Received post-visit callback  &amp; -0.085 &amp; 0.718 &amp; -0.119 &amp; 0.02 \\ 
##   Received post-visit offer for unit  &amp; -0.039 &amp; 0.718 &amp; -0.055 &amp; 0.123 \\ 
##   III. Punitive vs. Control (Lower Bound) &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; 0.007 &amp; 0.8 &amp; 0.009 &amp; 0.555 \\ 
##   Received post-visit callback  &amp; 0.019 &amp; 0.8 &amp; 0.024 &amp; 0.676 \\ 
##   Received post-visit offer for unit  &amp; 0.018 &amp; 0.8 &amp; 0.023 &amp; 0.696 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; 0.048 &amp; 0.8 &amp; 0.061 &amp; 0.806 \\ 
##   Received post-visit callback  &amp; -0.066 &amp; 0.8 &amp; -0.083 &amp; 0.056 \\ 
##   Received post-visit offer for unit  &amp; -0.021 &amp; 0.8 &amp; -0.026 &amp; 0.269 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; 0.039 &amp; 0.8 &amp; 0.047 &amp; 0.785 \\ 
##   Received post-visit callback  &amp; -0.085 &amp; 0.8 &amp; -0.107 &amp; 0.019 \\ 
##   Received post-visit offer for unit  &amp; -0.039 &amp; 0.8 &amp; -0.049 &amp; 0.122 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 17:10:30 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrr}
##   \hline
## Outcome &amp; ITT &amp; ITT\_D &amp; CACE &amp; p-value \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; -0.015 &amp; 0.81 &amp; -0.018 &amp; 0.388 \\ 
##   Received post-visit callback  &amp; -0.002 &amp; 0.81 &amp; -0.002 &amp; 0.486 \\ 
##   Received post-visit offer for unit  &amp; -0.003 &amp; 0.81 &amp; -0.004 &amp; 0.464 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; -0.061 &amp; 0.81 &amp; -0.075 &amp; 0.14 \\ 
##   Received post-visit callback  &amp; -0.036 &amp; 0.81 &amp; -0.045 &amp; 0.202 \\ 
##   Received post-visit offer for unit  &amp; -0.017 &amp; 0.81 &amp; -0.021 &amp; 0.31 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp; -0.089 &amp; 0.81 &amp; -0.106 &amp; 0.045 \\ 
##   Received post-visit callback  &amp; -0.035 &amp; 0.81 &amp; -0.043 &amp; 0.206 \\ 
##   Received post-visit offer for unit  &amp; -0.014 &amp; 0.81 &amp; -0.017 &amp; 0.328 \\ 
##   II. Punitive vs. Control (Upper and Lower Bounds) &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; 0.007 &amp; 0.718 &amp; 0.01 &amp; 0.555 \\ 
##   Lower bound &amp; 0.007 &amp; 0.8 &amp; 0.009 &amp; 0.555 \\ 
##   Received post-visit callback  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; 0.019 &amp; 0.718 &amp; 0.027 &amp; 0.675 \\ 
##   Lower bound &amp; 0.019 &amp; 0.8 &amp; 0.024 &amp; 0.676 \\ 
##   Received post-visit offer for unit  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; 0.018 &amp; 0.718 &amp; 0.025 &amp; 0.696 \\ 
##   Lower bound &amp; 0.018 &amp; 0.8 &amp; 0.023 &amp; 0.696 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; 0.048 &amp; 0.718 &amp; 0.067 &amp; 0.806 \\ 
##   Lower bound &amp; 0.048 &amp; 0.8 &amp; 0.061 &amp; 0.806 \\ 
##   Received post-visit callback  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; -0.066 &amp; 0.718 &amp; -0.092 &amp; 0.056 \\ 
##   Lower bound &amp; -0.066 &amp; 0.8 &amp; -0.083 &amp; 0.056 \\ 
##   Received post-visit offer for unit  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; -0.021 &amp; 0.718 &amp; -0.029 &amp; 0.269 \\ 
##   Lower bound &amp; -0.021 &amp; 0.8 &amp; -0.026 &amp; 0.269 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; 0.039 &amp; 0.718 &amp; 0.053 &amp; 0.785 \\ 
##   Lower bound &amp; 0.039 &amp; 0.8 &amp; 0.047 &amp; 0.785 \\ 
##   Received post-visit callback  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; -0.085 &amp; 0.718 &amp; -0.119 &amp; 0.02 \\ 
##   Lower bound &amp; -0.085 &amp; 0.8 &amp; -0.107 &amp; 0.019 \\ 
##   Received post-visit offer for unit  &amp;  &amp;  &amp;  &amp;  \\ 
##   Upper bound &amp; -0.039 &amp; 0.718 &amp; -0.055 &amp; 0.123 \\ 
##   Lower bound &amp; -0.039 &amp; 0.8 &amp; -0.049 &amp; 0.122 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a15-p.a-36-covariate-adjusted-itt-estimates" class="section level2">
<h2><span class="header-section-number">2.18</span> Table A15 (p. A-36): Covariate Adjusted ITT Estimates</h2>
<div id="lasso-regression-for-covariate-selection" class="section level3">
<h3><span class="header-section-number">2.18.1</span> Lasso Regression for Covariate Selection</h3>
<p>We first use the lasso as a principled method to select pre-treatment covariates that are prognostic of outcomes by arm (which are then used to estimate covariate adjusted ITT effects). The code below conducts this covariate selection procedure and produces the output files <code>misc_covselect_select0.csv</code>, <code>misc_covselect_select1.csv</code>, and <code>misc_covselect_select2.csv</code> (which are included in the replication archive). Note: This code chunk takes awhile to run.</p>
<pre class="r"><code>Ys &lt;- c(&quot;index.wb&quot;, &quot;ncb_wb&quot;, &quot;noff_wb&quot;,
        &quot;index.wh&quot;, &quot;ncb_wh&quot;, &quot;noff_wh&quot;,
        &quot;index.bh&quot;, &quot;ncb_bh&quot;, &quot;noff_bh&quot;)

llrace &lt;- c(&quot;primary_api&quot;, &quot;primary_blk&quot;, &quot;primary_hsp&quot;, &quot;primary_wht&quot;)
llage &lt;- c(&quot;primary_age_18to34&quot;, &quot;primary_age_35to44&quot;, &quot;primary_age_45to64&quot;, &quot;primary_age_65over&quot;, &quot;primary_age_unknown&quot;)
testerfe &lt;- c(&quot;tid.A01&quot;, &quot;tid.A10&quot;, &quot;tid.A11&quot;, &quot;tid.A13&quot;, &quot;tid.A02&quot;, &quot;tid.A21&quot;, &quot;tid.A22&quot;, &quot;tid.A03&quot;, &quot;tid.A04&quot;, &quot;tid.A05&quot;,
              &quot;tid.A06&quot;, &quot;tid.A07&quot;, &quot;tid.A08&quot;, &quot;tid.A09&quot;, &quot;tid.B01&quot;, &quot;tid.B11&quot;, &quot;tid.B12&quot;, &quot;tid.B14&quot;, &quot;tid.B16&quot;, &quot;tid.B02&quot;, 
              &quot;tid.B20&quot;, &quot;tid.B23&quot;, &quot;tid.B24&quot;, &quot;tid.B25&quot;, &quot;tid.B27&quot;, &quot;tid.B03&quot;, &quot;tid.B04&quot;, &quot;tid.B06&quot;, &quot;tid.B07&quot;, &quot;tid.B08&quot;, 
              &quot;tid.B09&quot;, &quot;tid.C01&quot;, &quot;tid.C10&quot;, &quot;tid.C12&quot;, &quot;tid.C13&quot;, &quot;tid.C14&quot;, &quot;tid.C15&quot;, &quot;tid.C02&quot;, &quot;tid.C27&quot;, &quot;tid.C29&quot;, 
              &quot;tid.C03&quot;, &quot;tid.C31&quot;, &quot;tid.C33&quot;, &quot;tid.C04&quot;, &quot;tid.C05&quot;, &quot;tid.C06&quot;, &quot;tid.C07&quot;, &quot;tid.C08&quot;, &quot;tid.C09&quot;)
tafe &lt;- testerfe[grepl(&quot;.A&quot;,testerfe,fixed=TRUE)]
tbfe &lt;- testerfe[grepl(&quot;.B&quot;,testerfe,fixed=TRUE)]
tcfe &lt;- testerfe[grepl(&quot;.C&quot;,testerfe,fixed=TRUE)]

Xs &lt;- c(&quot;frame&quot;, &quot;partnered&quot;, &quot;rent + m.rent&quot;, &quot;numbr&quot;, &quot;sqft + m.sqft&quot;,&quot;regime1&quot;,&quot;regime2&quot;,&quot;regime3&quot;,
        &quot;nnumattr_bh&quot;, &quot;nnumattr_wh&quot;, &quot;nnumattr_wb&quot;, &quot;nnumskep_bh&quot;, &quot;nnumskep_wh&quot;, &quot;nnumskep_wb&quot;, 
        &quot;npctskep_bh&quot;, &quot;npctskep_wh&quot;, &quot;npctskep_wb&quot;, &quot;nnumpos_bh&quot; , &quot;nnumpos_wh&quot; , &quot;nnumpos_wb&quot; , 
        &quot;nnumneu_bh&quot; , &quot;nnumneu_wh&quot; , &quot;nnumneu_wb&quot; , &quot;nnumneg_bh&quot; , &quot;nnumneg_wh&quot; , &quot;nnumneg_wb&quot; , 
        &quot;npctpos_bh&quot; , &quot;npctpos_wh&quot; , &quot;npctpos_wb&quot; , &quot;npctneu_bh&quot; , &quot;npctneu_wh&quot; , &quot;npctneu_wb&quot; , 
        &quot;npctneg_bh&quot; , &quot;npctneg_wh&quot; , &quot;npctneg_wb&quot; , &quot;nanyskep_bh&quot;, &quot;nanyskep_wh&quot;, &quot;nanyskep_wb&quot;, 
        &quot;nanyneg_bh&quot; , &quot;nanyneg_wh&quot; , &quot;nanyneg_wb&quot; , &quot;team_gender&quot;, &quot;broker&quot;,
        &quot;callorder.wb&quot;, &quot;callorder.wh&quot;, &quot;callorder.bh&quot;,
        &quot;incrank.wb.gt&quot;, &quot;incrank.wh.gt&quot;, &quot;incrank.bh.gt&quot;,
        &quot;incrank.wb.eq&quot;, &quot;incrank.wh.eq&quot;, &quot;incrank.bh.eq&quot;,
        &quot;incrank.wb.lt&quot;, &quot;incrank.wh.lt&quot;, &quot;incrank.bh.lt&quot;,
        &quot;boro.brx&quot;, &quot;boro.brk&quot;, &quot;boro.mnh&quot;, &quot;boro.que&quot;, &quot;boro.stn&quot;,
        &quot;inc.w.hi&quot;,&quot;inc.b.hi&quot;,&quot;inc.h.hi&quot;,&quot;ll.female + m.ll.female&quot;,
        llrace, llage, testerfe)

# Define sets of matched pair vars to kick out, by outcome
wb.vars &lt;- Xs[grepl(&quot;.wb&quot;,Xs,fixed=TRUE) | grepl(&quot;_wb&quot;,Xs,fixed=TRUE)]
wh.vars &lt;- Xs[grepl(&quot;.wh&quot;,Xs,fixed=TRUE) | grepl(&quot;_wh&quot;,Xs,fixed=TRUE)]
bh.vars &lt;- Xs[grepl(&quot;.bh&quot;,Xs,fixed=TRUE) | grepl(&quot;_bh&quot;,Xs,fixed=TRUE)]


# Change factor vars to numeric (needed for cross validation function)
dat$frame &lt;- as.numeric(dat$frame) # 1=likely disc, 2=representative (recoded to 0)
dat$frame &lt;- ifelse(dat$frame==2, 0, dat$frame)

dat$team_gender &lt;- as.numeric(dat$team_gender) # 1=female, 2=male (recoded to 0)
dat$team_gender &lt;- ifelse(dat$team_gender==2, 0, dat$team_gender)

# Subset data by treatment arm
sub0 &lt;- dat[dat$TA==0,]
sub1 &lt;- dat[dat$TA==1,]
sub2 &lt;- dat[dat$TA==2,]

#------------------------------------------------#
# Variable selection using lasso
#------------------------------------------------#
set.seed(20150210)
nfolds &lt;- 5
niter &lt;- 1000  # number of times to shuffle folds

# ============== #
# Z=0 (control)
# ============== #

# initialize object to store results
# nested lists of 9 (one per outcome)
# one for model selected at lambda.min (cv0.min)
# another for model selected at lambda.1se (cv0.1se)
# one for a final set of covs we choose (cv0)
cv0.min &lt;- cv0.1se &lt;- list()
cv0 &lt;- list()

# iterate over outcomes
  for(j in 1:length(Ys)){
  # cat(&quot;\n ******************* Outcome: &quot;, Ys[j] ,&quot; ******************* \n&quot;, sep=&quot;&quot;)
  # pre-process data conditional on outcome of interest
  if(j %in% 1:3) {
    subX &lt;- Xs[!(Xs %in% c(wh.vars, bh.vars))]  # if excluding hisp tester FE add: , tbfe
  } else if(j %in% 4:6) {
    subX &lt;- Xs[!(Xs %in% c(wb.vars, bh.vars))]  # if excluding black tester FE add: , tafe
  } else if(j %in% 7:9) {
    subX &lt;- Xs[!(Xs %in% c(wb.vars, wh.vars))]  # if excluding white tester FE add: , tcfe
  }
  temp &lt;- sub0[,names(sub0) %in% c(Ys[j], subX, &quot;ipw&quot;)]
  temp &lt;- temp[apply(temp, 1, function(x) sum(is.na(x))) == 0,]  # kick out missing (required for cv.glmnet)
    
  # initialize object to store
  cv.out &lt;- list()
  
  # initialize vector of fold assignments
  folds0 &lt;- c(rep(1, round(nrow(temp)/nfolds, 0)),
              rep(2, round(nrow(temp)/nfolds, 0)),
              rep(3, round(nrow(temp)/nfolds, 0)),
              rep(4, round(nrow(temp)/nfolds, 0)),
              rep(5, nrow(temp) - 4*round(nrow(temp)/nfolds, 0) ))
    
  # iterate over shuffled fold assignments
  for(i in 1:niter){
    # cat(i, &quot; &quot;, sep=&quot;&quot;)
    # shuffle folds
    fold.assign &lt;- folds0[sample(1:nrow(temp), nrow(temp), replace=FALSE)]
    # lasso
    cv.out[[i]] &lt;- cv.glmnet(x=as.matrix(temp[,!(names(temp) %in% c(Ys[j], &quot;ipw&quot;))]),
                             y=as.vector(temp[[Ys[j]]]),
                             weights=temp$ipw,
                             type.measure=&quot;mse&quot;,
                             nfolds=5,
                             foldid=fold.assign,
                             alpha=1)
  }
  # Grab variables associated with model at lambda.1se and lambda.min
  out.1se &lt;- lapply(cv.out, function(x) grabvars(x, s=&quot;lambda.1se&quot;))
  out.min &lt;- lapply(cv.out, function(x) grabvars(x, s=&quot;lambda.min&quot;))
  vars.1se &lt;- lapply(out.1se, function(x) as.character(x$vars[,1]))
  vars.min &lt;- lapply(out.min, function(x) as.character(x$vars[,1]))
  
  # For each candidate model, fit Y ~ X for the treatment arm of interest
  fit.1se &lt;- fit.min &lt;- list()
  
  for(k in 1:length(vars.1se)){
    # one SE rule
    if ( length(vars.1se[[k]]) &gt; 1 ) {
      X.selected &lt;- vars.1se[[k]][vars.1se[[k]] != &quot;(Intercept)&quot;]
      X.selected &lt;- paste(X.selected, collapse=&quot; + &quot;)
      model &lt;- paste(Ys[j], &quot;~&quot;, X.selected, sep=&quot; &quot;)
      fit &lt;- lm(formula=model, data=sub0)
      adjusted.r2 &lt;- summary(fit)$adj.r.squared
      f.stat &lt;- summary(fit)$fstatistic[1]
      f.pvalue &lt;- 1-pf(q=summary(fit)$fstatistic[1], df1=summary(fit)$fstatistic[2], df2=summary(fit)$fstatistic[3])
      fit.1se[[k]] &lt;- c(X.selected, adjusted.r2, f.stat, f.pvalue)      
    } else {
      adjusted.r2 &lt;- NA
      f.stat &lt;- NA
      f.pvalue &lt;- NA
      fit.1se[[k]] &lt;- c(NA, adjusted.r2, f.stat, f.pvalue)
    }
    
    # min lambda
    if ( length(vars.min[[k]]) &gt; 1 ) {
      X.selected &lt;- vars.min[[k]][vars.min[[k]] != &quot;(Intercept)&quot;]
      X.selected &lt;- paste(X.selected, collapse=&quot; + &quot;)
      model &lt;- paste(Ys[j], &quot;~&quot;, X.selected, sep=&quot; &quot;)
      fit &lt;- lm(formula=model, data=sub0)
      adjusted.r2 &lt;- summary(fit)$adj.r.squared
      f.stat &lt;- summary(fit)$fstatistic[1]
      f.pvalue &lt;- 1-pf(q=summary(fit)$fstatistic[1], df1=summary(fit)$fstatistic[2], df2=summary(fit)$fstatistic[3])
      fit.min[[k]] &lt;- c(X.selected, adjusted.r2, f.stat, f.pvalue)      
    } else {
      adjusted.r2 &lt;- NA
      f.stat &lt;- NA
      f.pvalue &lt;- NA
      fit.min[[k]] &lt;- c(NA, adjusted.r2, f.stat, f.pvalue)      
    }
  }
  
  # collect all results
  fit.min &lt;- do.call(rbind, fit.min)
  fit.1se &lt;- do.call(rbind, fit.1se)
  fit.min &lt;- as.data.frame(fit.min)
  fit.1se &lt;- as.data.frame(fit.1se)
  names(fit.1se) &lt;- names(fit.min) &lt;- c(&quot;variables&quot;,&quot;adj.r2&quot;,&quot;f.stat&quot;,&quot;f.pvalue&quot;)
  fit.min[,1] &lt;- as.character(fit.min[,1])
  fit.1se[,1] &lt;- as.character(fit.1se[,1])
  for(v in 2:ncol(fit.min)) fit.min[,v] &lt;- as.numeric(as.character(fit.min[,v]))
  for(v in 2:ncol(fit.1se)) fit.1se[,v] &lt;- as.numeric(as.character(fit.1se[,v]))
  
  # choose variables that are (1) not missing, (2) with highest adj R2, and (3) with significant f stat p value
  choose.min &lt;- fit.min[!is.na(fit.min$variables) &amp; fit.min$adj.r2==max(fit.min$adj.r2, na.rm=TRUE) &amp; fit.min$f.pvalue &lt; 0.05,]
  choose.1se &lt;- fit.1se[!is.na(fit.1se$variables) &amp; fit.1se$adj.r2==max(fit.1se$adj.r2, na.rm=TRUE) &amp; fit.1se$f.pvalue &lt; 0.05,]

  # store result - lambda min
  if(nrow(choose.min)==0){
    cv0.min[[j]] &lt;- rep(NA,4)
  } else {
    cv0.min[[j]] &lt;- choose.min &lt;- choose.min[!duplicated(choose.min),]
  }
  
  # store result - lambda 1se
  if(nrow(choose.1se)==0){
    cv0.1se[[j]] &lt;- rep(NA,4)
  } else {
    cv0.1se[[j]] &lt;- choose.1se &lt;- choose.1se[!duplicated(choose.1se),]
  }
}

# ============== #
# Z=1 (monitoring)
# ============== # 

# initialize object to store results
# nested lists of 9 (one per outcome)
# one for model selected at lambda.min (cv1.min)
# another for model selected at lambda.1se (cv1.1se)
# one for a final set of covs we choose (cv1)
cv1.min &lt;- cv1.1se &lt;- list()
# iterate over outcomes
  for(j in 1:length(Ys)){
  # cat(&quot;\n ******************* Outcome: &quot;, Ys[j] ,&quot; ******************* \n&quot;, sep=&quot;&quot;)
  # pre-process data conditional on outcome of interest
  if(j %in% 1:3) {
    subX &lt;- Xs[!(Xs %in% c(wh.vars, bh.vars))]  # if excluding hisp tester FE add: , tbfe
  } else if(j %in% 4:6) {
    subX &lt;- Xs[!(Xs %in% c(wb.vars, bh.vars))]  # if excluding black tester FE add: , tafe
  } else if(j %in% 7:9) {
    subX &lt;- Xs[!(Xs %in% c(wb.vars, wh.vars))]  # if excluding white tester FE add: , tcfe
  }
  temp &lt;- sub1[,names(sub1) %in% c(Ys[j], subX, &quot;ipw&quot;)]
  temp &lt;- temp[apply(temp, 1, function(x) sum(is.na(x))) == 0,]  # kick out missing (required for cv.glmnet)
  
  # initialize object to store
  cv.out &lt;- list()
  # initialize vector of fold assignments
  folds0 &lt;- c(rep(1, round(nrow(temp)/nfolds, 0)),
              rep(2, round(nrow(temp)/nfolds, 0)),
              rep(3, round(nrow(temp)/nfolds, 0)),
              rep(4, round(nrow(temp)/nfolds, 0)),
              rep(5, nrow(temp) - 4*round(nrow(temp)/nfolds, 0) ))
  # iterate over shuffled fold assignments
  for(i in 1:niter){
    # cat(i, &quot; &quot;, sep=&quot;&quot;)
    # shuffle folds
    fold.assign &lt;- folds0[sample(1:nrow(temp), nrow(temp), replace=FALSE)]
    # lasso
    cv.out[[i]] &lt;- cv.glmnet(x=as.matrix(temp[,!(names(temp) %in% c(Ys[j], &quot;ipw&quot;))]),
                             y=as.vector(temp[[Ys[j]]]),
                             weights=temp$ipw,
                             type.measure=&quot;mse&quot;,
                             nfolds=5,
                             foldid=fold.assign,
                             alpha=1)
  }
  # Grab variables associated with model at lambda.1se and lambda.min
  out.1se &lt;- lapply(cv.out, function(x) grabvars(x, s=&quot;lambda.1se&quot;))
  out.min &lt;- lapply(cv.out, function(x) grabvars(x, s=&quot;lambda.min&quot;))
  vars.1se &lt;- lapply(out.1se, function(x) as.character(x$vars[,1]))
  vars.min &lt;- lapply(out.min, function(x) as.character(x$vars[,1]))
  
  # For each candidate model, fit Y ~ X for the treatment arm of interest
  fit.1se &lt;- fit.min &lt;- list()
  for(k in 1:length(vars.1se)){
    # one SE rule
    if ( length(vars.1se[[k]]) &gt; 1 ) {
      X.selected &lt;- vars.1se[[k]][vars.1se[[k]] != &quot;(Intercept)&quot;]
      X.selected &lt;- paste(X.selected, collapse=&quot; + &quot;)
      model &lt;- paste(Ys[j], &quot;~&quot;, X.selected, sep=&quot; &quot;)
      fit &lt;- lm(formula=model, data=sub1)
      adjusted.r2 &lt;- summary(fit)$adj.r.squared
      f.stat &lt;- summary(fit)$fstatistic[1]
      f.pvalue &lt;- 1-pf(q=summary(fit)$fstatistic[1], df1=summary(fit)$fstatistic[2], df2=summary(fit)$fstatistic[3])
      fit.1se[[k]] &lt;- c(X.selected, adjusted.r2, f.stat, f.pvalue)      
    } else {
      adjusted.r2 &lt;- NA
      f.stat &lt;- NA
      f.pvalue &lt;- NA
      fit.1se[[k]] &lt;- c(NA, adjusted.r2, f.stat, f.pvalue)
    }
    # min lambda
    if ( length(vars.min[[k]]) &gt; 1 ) {
      X.selected &lt;- vars.min[[k]][vars.min[[k]] != &quot;(Intercept)&quot;]
      X.selected &lt;- paste(X.selected, collapse=&quot; + &quot;)
      model &lt;- paste(Ys[j], &quot;~&quot;, X.selected, sep=&quot; &quot;)
      fit &lt;- lm(formula=model, data=sub1)
      adjusted.r2 &lt;- summary(fit)$adj.r.squared
      f.stat &lt;- summary(fit)$fstatistic[1]
      f.pvalue &lt;- 1-pf(q=summary(fit)$fstatistic[1], df1=summary(fit)$fstatistic[2], df2=summary(fit)$fstatistic[3])
      fit.min[[k]] &lt;- c(X.selected, adjusted.r2, f.stat, f.pvalue)      
    } else {
      adjusted.r2 &lt;- NA
      f.stat &lt;- NA
      f.pvalue &lt;- NA
      fit.min[[k]] &lt;- c(NA, adjusted.r2, f.stat, f.pvalue)      
    }
  }
  
  # collect all results
  fit.min &lt;- do.call(rbind, fit.min)
  fit.1se &lt;- do.call(rbind, fit.1se)
  fit.min &lt;- as.data.frame(fit.min)
  fit.1se &lt;- as.data.frame(fit.1se)
  names(fit.1se) &lt;- names(fit.min) &lt;- c(&quot;variables&quot;,&quot;adj.r2&quot;,&quot;f.stat&quot;,&quot;f.pvalue&quot;)
  fit.min[,1] &lt;- as.character(fit.min[,1])
  fit.1se[,1] &lt;- as.character(fit.1se[,1])
  for(v in 2:ncol(fit.min)) fit.min[,v] &lt;- as.numeric(as.character(fit.min[,v]))
  for(v in 2:ncol(fit.1se)) fit.1se[,v] &lt;- as.numeric(as.character(fit.1se[,v]))
  
  # choose variables that are (1) not missing, (2) with highest adj R2, and (3) with significant f stat p value
  choose.min &lt;- fit.min[!is.na(fit.min$variables) &amp; fit.min$adj.r2==max(fit.min$adj.r2, na.rm=TRUE) &amp; fit.min$f.pvalue &lt; 0.05,]
  choose.1se &lt;- fit.1se[!is.na(fit.1se$variables) &amp; fit.1se$adj.r2==max(fit.1se$adj.r2, na.rm=TRUE) &amp; fit.1se$f.pvalue &lt; 0.05,]
  
  # store result - lambda min
  if(nrow(choose.min)==0){
    cv1.min[[j]] &lt;- rep(NA,4)
  } else {
    cv1.min[[j]] &lt;- choose.min &lt;- choose.min[!duplicated(choose.min),]
  }
  
  # store result - lambda 1se
  if(nrow(choose.1se)==0){
    cv1.1se[[j]] &lt;- rep(NA,4)
  } else {
    cv1.1se[[j]] &lt;- choose.1se &lt;- choose.1se[!duplicated(choose.1se),]
  }
  
  }

# ============== #
# Z=2 (punitive)
# ============== # 

# initialize object to store results
# nested lists of 9 (one per outcome)
# one for model selected at lambda.min (cv2.min)
# another for model selected at lambda.1se (cv2.1se)
# one for a final set of covs we choose (cv1)
cv2.min &lt;- cv2.1se &lt;- list()
# iterate over outcomes
  for(j in 1:length(Ys)){
  # cat(&quot;\n ******************* Outcome: &quot;, Ys[j] ,&quot; ******************* \n&quot;, sep=&quot;&quot;)
  # pre-process data conditional on outcome of interest
  if(j %in% 1:3) {
    subX &lt;- Xs[!(Xs %in% c(wh.vars, bh.vars))]  # if excluding hisp tester FE add: , tbfe
  } else if(j %in% 4:6) {
    subX &lt;- Xs[!(Xs %in% c(wb.vars, bh.vars))]  # if excluding black tester FE add: , tafe
  } else if(j %in% 7:9) {
    subX &lt;- Xs[!(Xs %in% c(wb.vars, wh.vars))]  # if excluding white tester FE add: , tcfe
  }
  temp &lt;- sub2[,names(sub2) %in% c(Ys[j], subX, &quot;ipw&quot;)]
  temp &lt;- temp[apply(temp, 1, function(x) sum(is.na(x))) == 0,]  # kick out missing (required for cv.glmnet)
  # initialize object to store
  cv.out &lt;- list()
  # initialize vector of fold assignments
  folds0 &lt;- c(rep(1, round(nrow(temp)/nfolds, 0)),
              rep(2, round(nrow(temp)/nfolds, 0)),
              rep(3, round(nrow(temp)/nfolds, 0)),
              rep(4, round(nrow(temp)/nfolds, 0)),
              rep(5, nrow(temp) - 4*round(nrow(temp)/nfolds, 0) ))
  # iterate over shuffled fold assignments
  for(i in 1:niter){
    # cat(i, &quot; &quot;, sep=&quot;&quot;)
    # shuffle folds
    fold.assign &lt;- folds0[sample(1:nrow(temp), nrow(temp), replace=FALSE)]
    # lasso
    cv.out[[i]] &lt;- cv.glmnet(x=as.matrix(temp[,!(names(temp) %in% c(Ys[j], &quot;ipw&quot;))]),
                             y=as.vector(temp[[Ys[j]]]),
                             weights=temp$ipw,
                             type.measure=&quot;mse&quot;,
                             nfolds=5,
                             foldid=fold.assign,
                             alpha=1)
  }
  # Grab variables associated with model at lambda.1se and lambda.min
  out.1se &lt;- lapply(cv.out, function(x) grabvars(x, s=&quot;lambda.1se&quot;))
  out.min &lt;- lapply(cv.out, function(x) grabvars(x, s=&quot;lambda.min&quot;))
  vars.1se &lt;- lapply(out.1se, function(x) as.character(x$vars[,1]))
  vars.min &lt;- lapply(out.min, function(x) as.character(x$vars[,1]))
  # For each candidate model, fit Y ~ X for the treatment arm of interest
  fit.1se &lt;- fit.min &lt;- list()
  for(k in 1:length(vars.1se)){
    # one SE rule
    if ( length(vars.1se[[k]]) &gt; 1 ) {
      X.selected &lt;- vars.1se[[k]][vars.1se[[k]] != &quot;(Intercept)&quot;]
      X.selected &lt;- paste(X.selected, collapse=&quot; + &quot;)
      model &lt;- paste(Ys[j], &quot;~&quot;, X.selected, sep=&quot; &quot;)
      fit &lt;- lm(formula=model, data=sub2)
      adjusted.r2 &lt;- summary(fit)$adj.r.squared
      f.stat &lt;- summary(fit)$fstatistic[1]
      f.pvalue &lt;- 1-pf(q=summary(fit)$fstatistic[1], df1=summary(fit)$fstatistic[2], df2=summary(fit)$fstatistic[3])
      fit.1se[[k]] &lt;- c(X.selected, adjusted.r2, f.stat, f.pvalue)      
    } else {
      adjusted.r2 &lt;- NA
      f.stat &lt;- NA
      f.pvalue &lt;- NA
      fit.1se[[k]] &lt;- c(NA, adjusted.r2, f.stat, f.pvalue)
    }
    
    # min lambda
    if ( length(vars.min[[k]]) &gt; 1 ) {
      X.selected &lt;- vars.min[[k]][vars.min[[k]] != &quot;(Intercept)&quot;]
      X.selected &lt;- paste(X.selected, collapse=&quot; + &quot;)
      model &lt;- paste(Ys[j], &quot;~&quot;, X.selected, sep=&quot; &quot;)
      fit &lt;- lm(formula=model, data=sub2)
      adjusted.r2 &lt;- summary(fit)$adj.r.squared
      f.stat &lt;- summary(fit)$fstatistic[1]
      f.pvalue &lt;- 1-pf(q=summary(fit)$fstatistic[1], df1=summary(fit)$fstatistic[2], df2=summary(fit)$fstatistic[3])
      fit.min[[k]] &lt;- c(X.selected, adjusted.r2, f.stat, f.pvalue)      
    } else {
      adjusted.r2 &lt;- NA
      f.stat &lt;- NA
      f.pvalue &lt;- NA
      fit.min[[k]] &lt;- c(NA, adjusted.r2, f.stat, f.pvalue)      
    }
  }
  
  # collect all results
  fit.min &lt;- do.call(rbind, fit.min)
  fit.1se &lt;- do.call(rbind, fit.1se)
  fit.min &lt;- as.data.frame(fit.min)
  fit.1se &lt;- as.data.frame(fit.1se)
  names(fit.1se) &lt;- names(fit.min) &lt;- c(&quot;variables&quot;,&quot;adj.r2&quot;,&quot;f.stat&quot;,&quot;f.pvalue&quot;)
  fit.min[,1] &lt;- as.character(fit.min[,1])
  fit.1se[,1] &lt;- as.character(fit.1se[,1])
  for(v in 2:ncol(fit.min)) fit.min[,v] &lt;- as.numeric(as.character(fit.min[,v]))
  for(v in 2:ncol(fit.1se)) fit.1se[,v] &lt;- as.numeric(as.character(fit.1se[,v]))
  
  # choose variables that are (1) not missing, (2) with highest adj R2, and (3) with significant f stat p value
  choose.min &lt;- fit.min[!is.na(fit.min$variables) &amp; fit.min$adj.r2==max(fit.min$adj.r2, na.rm=TRUE) &amp; fit.min$f.pvalue &lt; 0.05,]
  choose.1se &lt;- fit.1se[!is.na(fit.1se$variables) &amp; fit.1se$adj.r2==max(fit.1se$adj.r2, na.rm=TRUE) &amp; fit.1se$f.pvalue &lt; 0.05,]
  
  # store result - lambda min
  if(nrow(choose.min)==0){
    cv2.min[[j]] &lt;- rep(NA,4)
  } else {
    cv2.min[[j]] &lt;- choose.min &lt;- choose.min[!duplicated(choose.min),]
  }
  
  # store result - lambda 1se
  if(nrow(choose.1se)==0){
    cv2.1se[[j]] &lt;- rep(NA,4)
  } else {
    cv2.1se[[j]] &lt;- choose.1se &lt;- choose.1se[!duplicated(choose.1se),]
  }
  
  }

# Label items in list
names(cv0.1se) &lt;- names(cv1.1se) &lt;- names(cv2.1se) &lt;- Ys
names(cv0.min) &lt;- names(cv1.min) &lt;- names(cv2.min) &lt;- Ys

# For each, check for more than one result, if so check if the predictors are the same
#  Deduplicate selected variables (if multiple results returned)
for(i in 1:length(cv0.min)){

  # cv0.min
  if( !is.null(nrow(cv0.min[[i]])) ) {
    if ( nrow(cv0.min[[i]]) &gt; 1 ) {
      var.comp &lt;- strsplit(cv0.min[[i]][,1], &quot; + &quot;, fixed=TRUE)
      var.comp &lt;- lapply(var.comp, function(x) sort(x))
      # if there are the same # of vars then
      if ( apply(as.matrix(sapply(var.comp, length)), MARGIN=2, function(x) sum(length(unique(x)))==1) ) {
        # combine all
        var.comp &lt;- do.call(cbind, var.comp)
        # if all vars are the same
        if ( mean(apply(var.comp, MARGIN=1, function(x) sum(length(unique(x)))==1))==1 ) {
          cv0.min[[i]] &lt;- cv0.min[[i]][sample(1:nrow(cv0.min[[i]]), 1),]
        } 
      }
    }
  }
  
  # cv1.min
  if( !is.null(nrow(cv1.min[[i]])) ) {
    if ( nrow(cv1.min[[i]]) &gt; 1 ) {
      var.comp &lt;- strsplit(cv1.min[[i]][,1], &quot; + &quot;, fixed=TRUE)
      var.comp &lt;- lapply(var.comp, function(x) sort(x))
      # if there are the same # of vars then
      if ( apply(as.matrix(sapply(var.comp, length)), MARGIN=2, function(x) sum(length(unique(x)))==1) ) {
        # combine all
        var.comp &lt;- do.call(cbind, var.comp)
        # if all vars are the same
        if ( mean(apply(var.comp, MARGIN=1, function(x) sum(length(unique(x)))==1))==1 ) {
          cv1.min[[i]] &lt;- cv1.min[[i]][sample(1:nrow(cv1.min[[i]]), 1),]
        } 
      }
    }
  }
  
  # cv2.min
  if( !is.null(nrow(cv2.min[[i]])) ) {
    if ( nrow(cv2.min[[i]]) &gt; 1 ) {
      var.comp &lt;- strsplit(cv2.min[[i]][,1], &quot; + &quot;, fixed=TRUE)
      var.comp &lt;- lapply(var.comp, function(x) sort(x))
      # if there are the same # of vars then
      if ( apply(as.matrix(sapply(var.comp, length)), MARGIN=2, function(x) sum(length(unique(x)))==1) ) {
        # combine all
        var.comp &lt;- do.call(cbind, var.comp)
        # if all vars are the same
        if ( mean(apply(var.comp, MARGIN=1, function(x) sum(length(unique(x)))==1))==1 ) {
          cv2.min[[i]] &lt;- cv2.min[[i]][sample(1:nrow(cv2.min[[i]]), 1),]
        } 
      }
    }
  }
}

# Collapse into a matrix
select0 &lt;- do.call(rbind, cv0.min)
select1 &lt;- do.call(rbind, cv1.min)
select2 &lt;- do.call(rbind, cv2.min)

# Save output - these are the selected covariates by arm
write.csv(select0, &quot;misc_covselect_select0.csv&quot;, row.names=TRUE)
write.csv(select1, &quot;misc_covselect_select1.csv&quot;, row.names=TRUE)
write.csv(select2, &quot;misc_covselect_select2.csv&quot;, row.names=TRUE)</code></pre>
</div>
<div id="covariate-adjusted-itt-estimation" class="section level3">
<h3><span class="header-section-number">2.18.2</span> Covariate Adjusted ITT Estimation</h3>
<p>The following code estimates covariate adjusted ITT effects and bootstraps 95% confidence intervals. Note: This code chunk takes awhile to run. This code chunk also saves main results used to build Table A15, which is done in the next code chunk below.</p>
<pre class="r"><code>##=============================================================================##
## Covariate Adjustment
##=============================================================================##

# define outcomes 
Ys &lt;- c(&quot;index.wb&quot;, &quot;ncb_wb&quot;, &quot;noff_wb&quot;,
        &quot;index.wh&quot;, &quot;ncb_wh&quot;, &quot;noff_wh&quot;,
        &quot;index.bh&quot;, &quot;ncb_bh&quot;, &quot;noff_bh&quot;)

# read in covariates from variable selection procedure

select0 &lt;- read.csv(&quot;misc_covselect_select0.csv&quot;, header=TRUE, colClasses=&quot;character&quot;)
select1 &lt;- read.csv(&quot;misc_covselect_select1.csv&quot;, header=TRUE, colClasses=&quot;character&quot;)
select2 &lt;- read.csv(&quot;misc_covselect_select2.csv&quot;, header=TRUE, colClasses=&quot;character&quot;)

names(select0)[1] &lt;- names(select1)[1] &lt;- names(select2)[1] &lt;- &quot;Y&quot;

# create block FE dummy variables
block.dums &lt;- model.matrix(~ -1 + as.factor(block), data=dat)
colnames(block.dums) &lt;- paste(&quot;block&quot;, 1:17, sep=&quot;&quot;)
#head(block.dums)
dat &lt;- cbind(dat, block.dums)
#names(dat)

# subset data by treatment arm

sub0 &lt;- dat[dat$TA==0,]
sub1 &lt;- dat[dat$TA==1,]
sub2 &lt;- dat[dat$TA==2,]

# create vector of block fixed effect variables
blockfe &lt;- paste(&quot;block&quot;, 2:17, sep=&quot;&quot;)

# create probability of treatment (for 2 group estimator)
# if in regime 1, equal probability of treatment

dat$pt10 &lt;- ifelse(dat$regime==1, .5, ifelse(dat$regime==2, 1/3, .5))
dat$pt20 &lt;- ifelse(dat$regime==1, .5, ifelse(dat$regime==2, 1/3, .5))
dat$pt21 &lt;- ifelse(dat$regime==1, .5, ifelse(dat$regime==2, .5, .5))

# create ipw from probability of treatment (for 2 group estimator)

dat$ipw10 &lt;- ifelse(dat$TA == 1, 1/dat$pt10, ifelse(dat$TA==0, 1/(1-dat$pt10), NA))
dat$ipw20 &lt;- ifelse(dat$TA == 2, 1/dat$pt20, ifelse(dat$TA==0, 1/(1-dat$pt20), NA))
dat$ipw21 &lt;- ifelse(dat$TA == 2, 1/dat$pt21, ifelse(dat$TA==1, 1/(1-dat$pt21), NA))

# create vectors of covariates by treatment arm and outcome
c0 &lt;- lapply(strsplit(select0$variables, &quot; + &quot;, fixed=TRUE), function(x) c(x[!is.na(x)], blockfe))
c1 &lt;- lapply(strsplit(select1$variables, &quot; + &quot;, fixed=TRUE), function(x) c(x[!is.na(x)], blockfe))
c2 &lt;- lapply(strsplit(select2$variables, &quot; + &quot;, fixed=TRUE), function(x) c(x[!is.na(x)], blockfe))

# covariate adjusted estimates with empirical sandwich variance estimator

esv2g.10 &lt;- NULL
esv2g.20 &lt;- NULL
esv2g.21 &lt;- NULL

for(i in 1:length(Ys)){
  esv2g.10[[i]] &lt;- esv2g(data=dat,zvar=&quot;TA&quot;,Yvar=Ys[i],covs0=c0[[i]],covs1=c1[[i]],z1=1,z0=0,ipwvar=&quot;ipw10&quot;,ptvar=&quot;pt10&quot;)
  esv2g.20[[i]] &lt;- esv2g(data=dat,zvar=&quot;TA&quot;,Yvar=Ys[i],covs0=c0[[i]],covs1=c2[[i]],z1=2,z0=0,ipwvar=&quot;ipw20&quot;,ptvar=&quot;pt20&quot;)
  esv2g.21[[i]] &lt;- esv2g(data=dat,zvar=&quot;TA&quot;,Yvar=Ys[i],covs0=c1[[i]],covs1=c2[[i]],z1=2,z0=1,ipwvar=&quot;ipw21&quot;,ptvar=&quot;pt21&quot;)  
}


# bootstrap

set.seed(20150229) # set seed locally for reproducible bootstrap estimates

bs2g.10 &lt;- NULL
bs2g.20 &lt;- NULL
bs2g.21 &lt;- NULL

# Define loop functions to run in parallel
bsfun_1 &lt;- function(i) {
  bs(data=dat, sims=5000, zvar=&quot;TA&quot;, Yvar=Ys[i], z0=0, z1=1, X0=c0[[i]], X1=c1[[i]], ipwvar=&quot;ipw10&quot;,ptvar=&quot;pt10&quot;)
}

bsfun_2 &lt;- function(i) {
  bs(data=dat, sims=5000, zvar=&quot;TA&quot;, Yvar=Ys[i], z0=0, z1=2, X0=c0[[i]], X1=c2[[i]], ipwvar=&quot;ipw20&quot;,ptvar=&quot;pt20&quot;)
}

bsfun_3 &lt;- function(i) {
  bs(data=dat, sims=5000, zvar=&quot;TA&quot;, Yvar=Ys[i], z0=1, z1=2, X0=c1[[i]], X1=c2[[i]], ipwvar=&quot;ipw21&quot;,ptvar=&quot;pt21&quot;)  
}

bs2g.10 &lt;- parallel::mclapply(1:length(Ys), bsfun_1)
bs2g.20 &lt;- parallel::mclapply(1:length(Ys), bsfun_2)
bs2g.21 &lt;- parallel::mclapply(1:length(Ys), bsfun_3)

# assemble results into matrices

bs10.est &lt;- sapply(bs2g.10, function(x) unlist(x[1,]))
bs10.var &lt;- sapply(bs2g.10, function(x) unlist(x[2,]))
bs10.se &lt;- sapply(bs2g.10, function(x) unlist(x[3,]))

bs20.est &lt;- sapply(bs2g.20, function(x) unlist(x[1,]))
bs20.var &lt;- sapply(bs2g.20, function(x) unlist(x[2,]))
bs20.se &lt;- sapply(bs2g.20, function(x) unlist(x[3,]))

bs21.est &lt;- sapply(bs2g.21, function(x) unlist(x[1,]))
bs21.var &lt;- sapply(bs2g.21, function(x) unlist(x[2,]))
bs21.se &lt;- sapply(bs2g.21, function(x) unlist(x[3,]))

es10 &lt;- do.call(rbind, lapply(esv2g.10, function(x) c(x$est, x$se)))
es20 &lt;- do.call(rbind, lapply(esv2g.20, function(x) c(x$est, x$se)))
es21 &lt;- do.call(rbind, lapply(esv2g.21, function(x) c(x$est, x$se)))

# Save bootstrap estimates
save(bs10.est, bs10.var, bs10.se,
     bs20.est, bs20.var, bs20.se,
     bs21.est, bs21.var, bs21.se,
     file=&quot;misc_bootstrap_estimates.RData&quot;)

# Read in bootstrap estimates
load(&quot;misc_bootstrap_estimates.RData&quot;)

#============================================================================#
# calculate 95% confidence intervals -- bootstrap
# (1) first order normal approximation
# (2) basic bootstrap interval
# (3) bootstrap percentile interval
# (4) studentized bootstrap interval
# (5) adjusted bootstrap percentile (BCa) interval
#============================================================================#


#==========================#
# (1) normal approximation
#==========================#

# if the ITT estimate is approximately normal, then
# 95% CI is  ITT.hat - b +/- z(alpha)*sqrt(nu)
# where z(alpha) = critical value of z at alpha=0.05
# ITT.hat = estimate (non-bootstrapped, get this from the empirical sandwich estimation output)
# b = mean of bootstrap estimate - ITT.hat
# nu = (1/(R-1))* SUM (over draws) (estimated mean - mean of bootstrap estimates)^2
# R = number of bootstrap draws (sims=5000 for us)

R = 5000

# M-C
bs10.normalci &lt;- list()
for(i in 1:length(Ys)){
  nu &lt;- (1/(R-1)) *   sum((bs10.est[,i]-mean(bs10.est[,i]))^2)
  lci &lt;- es10[i,1] - (mean(bs10.est[,i]) - es10[i,1]) - qnorm(p=0.975, mean=0, sd=1) * sqrt(nu)
  uci &lt;- es10[i,1] - (mean(bs10.est[,i]) - es10[i,1]) + qnorm(p=0.975, mean=0, sd=1) * sqrt(nu)
  bs10.normalci[[i]] &lt;- c(lci, uci)
}

# P-C
bs20.normalci &lt;- list()
for(i in 1:length(Ys)){
  nu &lt;- (1/(R-1)) *   sum((bs20.est[,i]-mean(bs20.est[,i]))^2)
  lci &lt;- es20[i,1] - (mean(bs20.est[,i]) - es20[i,1]) - qnorm(p=0.975, mean=0, sd=1) * sqrt(nu)
  uci &lt;- es20[i,1] - (mean(bs20.est[,i]) - es20[i,1]) + qnorm(p=0.975, mean=0, sd=1) * sqrt(nu)
  bs20.normalci[[i]] &lt;- c(lci, uci)
}

# P-M
bs21.normalci &lt;- list()
for(i in 1:length(Ys)){
  nu &lt;- (1/(R-1)) *   sum((bs21.est[,i]-mean(bs21.est[,i]))^2)
  lci &lt;- es21[i,1] - (mean(bs21.est[,i]) - es21[i,1]) - qnorm(p=0.975, mean=0, sd=1) * sqrt(nu)
  uci &lt;- es21[i,1] - (mean(bs21.est[,i]) - es21[i,1]) + qnorm(p=0.975, mean=0, sd=1) * sqrt(nu)
  bs21.normalci[[i]] &lt;- c(lci, uci)
}


bs10.normalci &lt;- do.call(rbind, bs10.normalci)
bs20.normalci &lt;- do.call(rbind, bs20.normalci)
bs21.normalci &lt;- do.call(rbind, bs21.normalci)

# bs10.normalci; bs20.normalci; bs21.normalci

#==========================#
# (2) basic bootstrap interval
#==========================#

# 2*t0 - quantile(t, c(.975, .025))

bs10.basicci &lt;- list()
bs20.basicci &lt;- list()
bs21.basicci &lt;- list()

for(i in 1:length(Ys)){
  bs10.basicci[[i]] &lt;- 2*es10[i,1] - quantile(bs10.est[,i], c(.975, .025))
  bs20.basicci[[i]] &lt;- 2*es20[i,1] - quantile(bs20.est[,i], c(.975, .025))
  bs21.basicci[[i]] &lt;- 2*es21[i,1] - quantile(bs21.est[,i], c(.975, .025))
}

bs10.basicci &lt;- do.call(rbind, bs10.basicci)
bs20.basicci &lt;- do.call(rbind, bs20.basicci)
bs21.basicci &lt;- do.call(rbind, bs21.basicci)
colnames(bs10.basicci) &lt;- colnames(bs20.basicci) &lt;- colnames(bs21.basicci) &lt;- c(&quot;lower&quot;,&quot;upper&quot;)

# bs10.basicci; bs20.basicci; bs21.basicci

#==========================#
# (3) percentile interval
#==========================#

# use empirical quantities at the 2.5th and 97.5th percentiles

bs10.pctci &lt;- t(apply(bs10.est, 2, function(x) quantile(x, c(.025, .975)))) 
bs20.pctci &lt;- t(apply(bs20.est, 2, function(x) quantile(x, c(.025, .975))))
bs21.pctci &lt;- t(apply(bs21.est, 2, function(x) quantile(x, c(.025, .975))))

# bs10.pctci; bs20.pctci; bs21.pctci



#==========================#
# (4) studentized boot ci
#==========================#

# generalization of student t statistic to bootstrap setting
# requires variance for bootstrap ITT etsimates computed from each bs sample

# first calculate test statistic for each bs sample
bs10.t &lt;- bs20.t &lt;- bs21.t &lt;- matrix(NA, ncol=length(Ys), nrow=R)

for(i in 1:length(Ys)){
  bs10.t[,i] &lt;- (bs10.est[,i] - es10[i,1])/bs10.se[,i]
  bs20.t[,i] &lt;- (bs20.est[,i] - es20[i,1])/bs20.se[,i]
  bs21.t[,i] &lt;- (bs21.est[,i] - es21[i,1])/bs21.se[,i]
}

# from the empirical distribution of the test statistics,
# we calculate order statistics/quantiles:
#   t*[(R+1)(1-alpha)]
#   t*[(R+1)(alpha)]

bs10.q &lt;- apply(bs10.t, 2, function(x) quantile(x, c(.975, .025)))
bs20.q &lt;- apply(bs20.t, 2, function(x) quantile(x, c(.975, .025)))
bs21.q &lt;- apply(bs21.t, 2, function(x) quantile(x, c(.975, .025)))

# the studentized CI is given by
# lower: ITT.hat - SE.hat * t*[(R+1)(1-alpha)]
# upper: ITT.hat - SE.hat * t*[(R+1)(alpha)]

bs10.tci &lt;- bs20.tci &lt;- bs21.tci &lt;- NULL
for(i in 1:length(Ys)){
  bs10.tci[[i]] &lt;- es10[i,1] - sd(bs10.est[,i]) * bs10.q[,i]
  bs20.tci[[i]] &lt;- es20[i,1] - sd(bs20.est[,i]) * bs20.q[,i]
  bs21.tci[[i]] &lt;- es21[i,1] - sd(bs21.est[,i]) * bs21.q[,i]  
}

bs10.tci &lt;- do.call(rbind, bs10.tci)
bs20.tci &lt;- do.call(rbind, bs20.tci)
bs21.tci &lt;- do.call(rbind, bs21.tci)
colnames(bs10.tci) &lt;- colnames(bs20.tci) &lt;- colnames(bs21.tci) &lt;- c(&quot;lower&quot;, &quot;upper&quot;)

# bs10.tci; bs20.tci; bs21.tci



#============================================================================#
# calculate 95% confidence intervals -- empirical sandwich variance estimator
#============================================================================#

# calculate df by model

n10 &lt;- rep(NA, length(Ys))
n20 &lt;- rep(NA, length(Ys))
n21 &lt;- rep(NA, length(Ys))

for(i in 1:length(Ys)){
  n10[i] &lt;- sum(apply(dat[dat$TA %in% c(0,1),names(dat) %in% c(Ys[i],c0[[i]],c1[[i]],&quot;TA&quot;)] ,1,function(x) sum(is.na(x))==0))
  n20[i] &lt;- sum(apply(dat[dat$TA %in% c(0,2),names(dat) %in% c(Ys[i],c0[[i]],c2[[i]],&quot;TA&quot;)] ,1,function(x) sum(is.na(x))==0))
  n21[i] &lt;- sum(apply(dat[dat$TA %in% c(1,2),names(dat) %in% c(Ys[i],c1[[i]],c2[[i]],&quot;TA&quot;)] ,1,function(x) sum(is.na(x))==0))  
}

# calculate DF as the two-group sample size, minus the sum of the number of covariates across treatment arms,
# minus 3 (2 for the intercepts of arm-specific models; 1 for the treatment-control difference)
df10 &lt;- n10 - (sapply(c0, length)+sapply(c1, length)) - 3
df20 &lt;- n20 - (sapply(c0, length)+sapply(c2, length)) - 3
df21 &lt;- n21 - (sapply(c1, length)+sapply(c2, length)) - 3

cv10 &lt;- -qt(p=0.025, df=df10, lower.tail=TRUE)
cv20 &lt;- -qt(p=0.025, df=df20, lower.tail=TRUE)
cv21 &lt;- -qt(p=0.025, df=df21, lower.tail=TRUE)

# form confidence interval

es10.ci &lt;- cbind(es10[,1]-abs(cv10)*es10[,2], es10[,1]+abs(cv10)*es10[,2])
es20.ci &lt;- cbind(es20[,1]-abs(cv20)*es20[,2], es20[,1]+abs(cv20)*es20[,2])
es21.ci &lt;- cbind(es21[,1]-abs(cv21)*es21[,2], es21[,1]+abs(cv21)*es21[,2])


#============================================================================#
# plot estimates and confidence intervals
#============================================================================#

histYlabs &lt;- c(&quot;Interactions index\n(White-Black)&quot;, &quot;Callback\n(White-Black)&quot;, &quot;Offer\n(White-Black)&quot;,
               &quot;Interactions index\n(White-Hispanic)&quot;, &quot;Callback\n(White-Hispanic)&quot;, &quot;Offer\n(White-Hispanic)&quot;,
               &quot;Interactions index\n(Black-Hispanic)&quot;, &quot;Callback\n(Black-Hispanic)&quot;, &quot;Offer\n(Black-Hispanic)&quot;)

# cbind(Ys, histYlabs)

xrange &lt;- max(abs(range(c(es10[,1], bs10.est))))
xrange &lt;- c(-xrange, xrange)

pdf(&quot;misc_bootstrap_bs_10_est.pdf&quot;, height=10, width=10)
#par(mfrow=c(3,3))
layout(matrix(c(1:9,0,10,0), nrow=4, ncol=3, byrow=TRUE), widths=c(3,3,3), heights=c(3,3,3,3), respect=FALSE)
for(i in 1:length(Ys)){
  hist(bs10.est[,i], breaks=55, main=histYlabs[i], xlab=&quot;bootstrap ITT estimate \n (monitoring v. control)&quot;, xlim=xrange, cex.axis=0.8)
  abline(v=es10[i,1], col=&quot;red&quot;, lwd=2, lty=1)  
  abline(v=mean(bs10.est[,i]), col=&quot;black&quot;, lwd=2, lty=2)
  abline(v=c(mean(bs10.est[,i])-sd(bs10.est[,i]), mean(bs10.est[,i])+sd(bs10.est[,i])), col=&quot;grey80&quot;, lwd=1, lty=4)  
  abline(v=es10.ci[i,], col=&quot;grey80&quot;, lwd=2, lty=2)
  abline(v=bs10.basicci[i,], col=rgb(1,0,1,.5), lty=2)
  abline(v=bs10.pctci[i,], col=rgb(0,0,1,.3), lty=3)
  abline(v=bs10.normalci[i,], col=rgb(0,1,0,.5), lty=4)
  abline(v=bs10.tci[i,], col=rgb(1,0,0,.3), lty=2)  
}
plot(c(0,10), c(0,10), pch=&quot;&quot;, xaxt=&quot;n&quot;, yaxt=&quot;n&quot;, main=&quot;&quot;, xlab=&quot;&quot;, ylab=&quot;&quot;, bty=&quot;n&quot;)
legend(0,10,legend=c(&quot;ITT Estimate&quot;,
                     &quot;Mean of Bootstrap ITT Estimates&quot;,
                     &quot;+/- 1 sd, Bootstrap ITT Estimates&quot;,
                     &quot;95% CI, Empirical Sandwich Var.&quot;,
                     &quot;95% CI, Basic Bootstrap&quot;,                    
                     &quot;95% CI, Percentile Bootstrap&quot;,
                     &quot;95% CI, Normal Approx. Bootstrap&quot;,
                     &quot;95% CI, Studentized Bootstrap&quot;),
       col=c(&quot;red&quot;, &quot;black&quot;, &quot;grey80&quot;, &quot;grey80&quot;, rgb(1,0,1,.5), rgb(0,0,1,.3), rgb(0,1,0,.5), rgb(1,0,0,.3)),
       lwd=rep(1.5, 8),
       lty=c(1,2,4,2,2,3,4,2),
       bty=&quot;n&quot;,
       seg.len=4)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code>xrange &lt;- max(abs(range(c(es20[,1], bs20.est))))
xrange &lt;- c(-xrange, xrange)

pdf(&quot;misc_bootstrap_bs_20_est.pdf&quot;, height=10, width=10)
#par(mfrow=c(3,3))
layout(matrix(c(1:9,0,10,0), nrow=4, ncol=3, byrow=TRUE), widths=c(3,3,3), heights=c(3,3,3,3), respect=FALSE)
for(i in 1:length(Ys)){
  hist(bs20.est[,i], breaks=55, main=histYlabs[i], xlab=&quot;bootstrap ITT estimate \n (punitive v. control)&quot;, xlim=xrange, cex.axis=0.8)
  abline(v=es20[i,1], col=&quot;red&quot;, lwd=2, lty=1)  
  abline(v=mean(bs20.est[,i]), col=&quot;black&quot;, lwd=2, lty=2)
  abline(v=c(mean(bs20.est[,i])-sd(bs20.est[,i]), mean(bs20.est[,i])+sd(bs20.est[,i])), col=&quot;grey80&quot;, lwd=1, lty=4)  
  abline(v=es20.ci[i,], col=&quot;grey80&quot;, lwd=2, lty=2)
  abline(v=bs20.basicci[i,], col=rgb(1,0,1,.5), lty=2)
  abline(v=bs20.pctci[i,], col=rgb(0,0,1,.3), lty=3)
  abline(v=bs20.normalci[i,], col=rgb(0,1,0,.5), lty=4)
  abline(v=bs20.tci[i,], col=rgb(1,0,0,.3), lty=2)  
}
plot(c(0,10), c(0,10), pch=&quot;&quot;, xaxt=&quot;n&quot;, yaxt=&quot;n&quot;, main=&quot;&quot;, xlab=&quot;&quot;, ylab=&quot;&quot;, bty=&quot;n&quot;)
legend(0,10,legend=c(&quot;ITT Estimate&quot;,
                     &quot;Mean of Bootstrap ITT Estimates&quot;,
                     &quot;+/- 1 sd, Bootstrap ITT Estimates&quot;,
                     &quot;95% CI, Empirical Sandwich Var.&quot;,
                     &quot;95% CI, Basic Bootstrap&quot;,                    
                     &quot;95% CI, Percentile Bootstrap&quot;,
                     &quot;95% CI, Normal Approx. Bootstrap&quot;,
                     &quot;95% CI, Studentized Bootstrap&quot;),
       col=c(&quot;red&quot;, &quot;black&quot;, &quot;grey80&quot;, &quot;grey80&quot;, rgb(1,0,1,.5), rgb(0,0,1,.3), rgb(0,1,0,.5), rgb(1,0,0,.3)),
       lwd=rep(1.5, 8),
       lty=c(1,2,4,2,2,3,4,2),
       bty=&quot;n&quot;,
       seg.len=4)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code>xrange &lt;- max(abs(range(c(es21[,1], bs21.est))))
xrange &lt;- c(-xrange, xrange)

pdf(&quot;misc_bootstrap_bs_21_est.pdf&quot;, height=10, width=10)
#par(mfrow=c(3,3))
layout(matrix(c(1:9,0,10,0), nrow=4, ncol=3, byrow=TRUE), widths=c(3,3,3), heights=c(3,3,3,3), respect=FALSE)
for(i in 1:length(Ys)){
  hist(bs21.est[,i], breaks=55, main=histYlabs[i], xlab=&quot;bootstrap ITT estimate \n (punitive v. monitoring)&quot;, xlim=xrange, cex.axis=0.8)
  abline(v=es21[i,1], col=&quot;red&quot;, lwd=2, lty=1)  
  abline(v=mean(bs21.est[,i]), col=&quot;black&quot;, lwd=2, lty=2)
  abline(v=c(mean(bs21.est[,i])-sd(bs21.est[,i]), mean(bs21.est[,i])+sd(bs21.est[,i])), col=&quot;grey80&quot;, lwd=1, lty=4)  
  abline(v=es21.ci[i,], col=&quot;grey80&quot;, lwd=2, lty=2)
  abline(v=bs21.basicci[i,], col=rgb(1,0,1,.5), lty=2)
  abline(v=bs21.pctci[i,], col=rgb(0,0,1,.3), lty=3)
  abline(v=bs21.normalci[i,], col=rgb(0,1,0,.5), lty=4)
  abline(v=bs21.tci[i,], col=rgb(1,0,0,.3), lty=2)  
}
plot(c(0,10), c(0,10), pch=&quot;&quot;, xaxt=&quot;n&quot;, yaxt=&quot;n&quot;, main=&quot;&quot;, xlab=&quot;&quot;, ylab=&quot;&quot;, bty=&quot;n&quot;)
legend(0,10,legend=c(&quot;ITT Estimate&quot;,
                     &quot;Mean of Bootstrap ITT Estimates&quot;,
                     &quot;+/- 1 sd, Bootstrap ITT Estimates&quot;,
                     &quot;95% CI, Empirical Sandwich Var.&quot;,
                     &quot;95% CI, Basic Bootstrap&quot;,                    
                     &quot;95% CI, Percentile Bootstrap&quot;,
                     &quot;95% CI, Normal Approx. Bootstrap&quot;,
                     &quot;95% CI, Studentized Bootstrap&quot;),
       col=c(&quot;red&quot;, &quot;black&quot;, &quot;grey80&quot;, &quot;grey80&quot;, rgb(1,0,1,.5), rgb(0,0,1,.3), rgb(0,1,0,.5), rgb(1,0,0,.3)),
       lwd=rep(1.5, 8),
       lty=c(1,2,4,2,2,3,4,2),
       bty=&quot;n&quot;,
       seg.len=4)
dev.off()</code></pre>
<pre><code>## quartz_off_screen 
##                 2</code></pre>
<pre class="r"><code>#============================================================================#
# assemble table
#============================================================================#

# Prep labels for output tables -- outcome labels, table section titles, table subsection titles

Ylabs &lt;- c(&quot;Index measure of favorable in-person interactions&quot;,
           &quot;Received post-visit callback&quot;,
           &quot;Received post-visit offer&quot;)
Ylabs &lt;- rep(Ylabs, 9)
# Ylabs

seclabs &lt;- c(&quot;I. Monitoring vs. Control&quot;, &quot;II. Punitive vs. Control&quot;, &quot;III. Punitive vs. Monitoring&quot;)
sublabs &lt;- rep(c(&quot;A. White vs. Black&quot;, &quot;B. White vs. Hispanic&quot;, &quot;C. Black vs. Hispanic&quot;), 3)

# Covariate adjusted estimates - table shell:
#   Col 1 = Covariate adj ITT estimate
#   Col 2 = Empirical sandwich SE estimate
#   Col 3 = Empirical sandwich 95% CI
#   Col 4 = Mean of bootstrap ITT estimates
#   Col 5 = SD of bootstrap ITT estimates
#   Col 6 = 95% CI - Studentized t
#   Col 7 = 95% CI - Basic bootstrap
#   Col 8 = 95% CI - Percentile bootstrap
#   Col 9 = 95% CI - Normal Approx

tab10 &lt;- cbind(round(es10, 3),
               apply(es10.ci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               round(apply(bs10.est, 2, mean),3),
               round(apply(bs10.est, 2, sd),3),
               apply(bs10.tci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs10.basicci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs10.pctci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs10.normalci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) )
)
# tab10

tab20 &lt;- cbind(round(es20, 3),
               apply(es20.ci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               round(apply(bs20.est, 2, mean),3),
               round(apply(bs20.est, 2, sd),3),
               apply(bs20.tci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs20.basicci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs20.pctci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs20.normalci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) )
)
# tab20

tab21 &lt;- cbind(round(es21, 3),
               apply(es21.ci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               round(apply(bs21.est, 2, mean),3),
               round(apply(bs21.est, 2, sd),3),
               apply(bs21.tci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs21.basicci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs21.pctci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) ),
               apply(bs21.normalci, 1, function(x) paste(&quot;[&quot;,round(x[1],3),&quot;,&quot;,round(x[2],3),&quot;]&quot;,sep=&quot;&quot;) )
)
# tab21


# stack panels into single table
tab.out &lt;- rbind(tab10, tab20, tab21)

# add parentheses to SE estimates
tab.out[,2] &lt;- paste(&quot;(&quot;, tab.out[,2], &quot;)&quot;, sep=&quot;&quot;)
tab.out[,5] &lt;- paste(&quot;(&quot;, tab.out[,5], &quot;)&quot;, sep=&quot;&quot;)

tab.out &lt;- cbind(Ylabs, tab.out)

colnames(tab.out) &lt;- c(&quot;Outcome Measure&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;95% CI&quot;, &quot;Mean Bootstrap Est.&quot;, &quot;Bootstrap SE&quot;, &quot;Studentized&quot;, &quot;Basic&quot;, &quot;Percentile&quot;, &quot;Normal Approx.&quot;)

# tab.out

# Write an unformatted table
write.csv(tab.out, &quot;out_tablea15_itt_blockfe_covadj_full_UNFORMATTED.csv&quot;, row.names=FALSE)

start.rows &lt;- seq(1,27,3)
stop.rows &lt;- start.rows + 2
# start.rows; stop.rows

tab.out2 &lt;- list()
seclabs.index &lt;- 0
for(i in 1:length(start.rows)){
  if(i %in% c(1,4,7)){
    seclabs.index &lt;- seclabs.index + 1
    tab.out2[[i]] &lt;- rbind(c(seclabs[seclabs.index], rep(NA,9)),
                           c(sublabs[i], rep(NA,9)),
                           tab.out[start.rows[i]:stop.rows[i],])        
  } else {
    tab.out2[[i]] &lt;- rbind(c(sublabs[i], rep(NA,9)), tab.out[start.rows[i]:stop.rows[i],])    
  }
}


tab.out2 &lt;- do.call(rbind, tab.out2)
# tab.out2

## Export results to csv

write.csv(tab.out2, &quot;out_tablea15_itt_blockfe_covadj_full.csv&quot;, row.names=FALSE)</code></pre>
<p>The following code builds Table A15 which includes the covariate adjusted ITT estimates (produced by the code above) and the unadjusted ITT estimates (from Table A7, for comparison)</p>
<pre class="r"><code>### Unadjusted estimates (two-group estimator)

un &lt;- read.csv(&quot;out_tablea7_itt_2g_blockfe_nocovs.csv&quot;,
               header=TRUE, stringsAsFactors = FALSE)
un &lt;- un[,c(1,2,3,6)]

### Covariate adjusted estimates (two-group estimator)

ca &lt;- read.csv(&quot;out_tablea15_itt_blockfe_covadj_full.csv&quot;,
               header=TRUE, stringsAsFactors=FALSE)

# get rid of normal approx CIs
ca &lt;- ca[,-10]

### Combine unadjusted with covariate adjusted
x &lt;- cbind(ca[,1], un[,2:4], ca[,2:9])

# clean up colnames
names(x) &lt;- c(&quot;Outcome Measure&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;95% CI&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;95% CI&quot;, &quot;Mean&quot;, &quot;SE&quot;, &quot;95% Studentized CI&quot;, &quot;95% Basic CI&quot;, &quot;95% Percentile CI&quot;)

# Show table in Rmd output
kable(x, col.names=c(&quot;Outcome Measure&quot;, &quot;Unadj. (Est.)&quot;, &quot;Unadj. (SE)&quot;, &quot;Unadj. (95% CI)&quot;,
                     &quot;Cov adj (Est.)&quot;, &quot;Cov adj (SE)&quot;, &quot;Cov adj (95% CI)&quot;,
                     &quot;Cov adj with bootstrap (Mean)&quot;, &quot;Cov adj with bootstrap (SE)&quot;,
                     &quot;Cov adj with bootstrap (95% Studentized CI)&quot;,
                     &quot;Cov adj with bootstrap (95% Basic CI)&quot;,
                     &quot;Cov adj with bootstrap (95% Percentile CI)&quot;),
      caption=&quot;**Table A15.**&quot;)</code></pre>
<table>
<caption><strong>Table A15.</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome Measure</th>
<th align="right">Unadj. (Est.)</th>
<th align="right">Unadj. (SE)</th>
<th align="left">Unadj. (95% CI)</th>
<th align="right">Cov adj (Est.)</th>
<th align="left">Cov adj (SE)</th>
<th align="left">Cov adj (95% CI)</th>
<th align="right">Cov adj with bootstrap (Mean)</th>
<th align="left">Cov adj with bootstrap (SE)</th>
<th align="left">Cov adj with bootstrap (95% Studentized CI)</th>
<th align="left">Cov adj with bootstrap (95% Basic CI)</th>
<th align="left">Cov adj with bootstrap (95% Percentile CI)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">I. Monitoring vs. Control</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">A. White vs. Black</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">-0.015</td>
<td align="right">0.052</td>
<td align="left">[-0.117, 0.087]</td>
<td align="right">-0.008</td>
<td align="left">(0.045)</td>
<td align="left">[-0.096,0.079]</td>
<td align="right">-0.009</td>
<td align="left">(0.054)</td>
<td align="left">[-0.14,0.127]</td>
<td align="left">[-0.114,0.1]</td>
<td align="left">[-0.117,0.098]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="right">-0.002</td>
<td align="right">0.045</td>
<td align="left">[-0.091, 0.088]</td>
<td align="right">-0.018</td>
<td align="left">(0.042)</td>
<td align="left">[-0.1,0.065]</td>
<td align="right">-0.021</td>
<td align="left">(0.05)</td>
<td align="left">[-0.133,0.102]</td>
<td align="left">[-0.113,0.081]</td>
<td align="left">[-0.117,0.077]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer</td>
<td align="right">-0.003</td>
<td align="right">0.036</td>
<td align="left">[-0.075, 0.068]</td>
<td align="right">-0.015</td>
<td align="left">(0.033)</td>
<td align="left">[-0.079,0.049]</td>
<td align="right">-0.016</td>
<td align="left">(0.035)</td>
<td align="left">[-0.092,0.061]</td>
<td align="left">[-0.084,0.054]</td>
<td align="left">[-0.084,0.054]</td>
</tr>
<tr class="even">
<td align="left">B. White vs. Hispanic</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">-0.061</td>
<td align="right">0.057</td>
<td align="left">[-0.172, 0.05]</td>
<td align="right">-0.098</td>
<td align="left">(0.051)</td>
<td align="left">[-0.198,0.003]</td>
<td align="right">-0.090</td>
<td align="left">(0.06)</td>
<td align="left">[-0.254,0.036]</td>
<td align="left">[-0.222,0.019]</td>
<td align="left">[-0.214,0.026]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="right">-0.036</td>
<td align="right">0.043</td>
<td align="left">[-0.121, 0.049]</td>
<td align="right">-0.034</td>
<td align="left">(0.042)</td>
<td align="left">[-0.116,0.048]</td>
<td align="right">-0.032</td>
<td align="left">(0.046)</td>
<td align="left">[-0.136,0.069]</td>
<td align="left">[-0.124,0.057]</td>
<td align="left">[-0.124,0.057]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer</td>
<td align="right">-0.017</td>
<td align="right">0.034</td>
<td align="left">[-0.084, 0.05]</td>
<td align="right">-0.021</td>
<td align="left">(0.032)</td>
<td align="left">[-0.083,0.042]</td>
<td align="right">-0.021</td>
<td align="left">(0.035)</td>
<td align="left">[-0.096,0.057]</td>
<td align="left">[-0.089,0.049]</td>
<td align="left">[-0.09,0.047]</td>
</tr>
<tr class="even">
<td align="left">C. Black vs. Hispanic</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">-0.089</td>
<td align="right">0.052</td>
<td align="left">[-0.192, 0.014]</td>
<td align="right">-0.095</td>
<td align="left">(0.048)</td>
<td align="left">[-0.189,0]</td>
<td align="right">-0.086</td>
<td align="left">(0.055)</td>
<td align="left">[-0.237,0.023]</td>
<td align="left">[-0.216,0.006]</td>
<td align="left">[-0.196,0.026]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="right">-0.035</td>
<td align="right">0.042</td>
<td align="left">[-0.118, 0.048]</td>
<td align="right">-0.005</td>
<td align="left">(0.039)</td>
<td align="left">[-0.083,0.073]</td>
<td align="right">0.000</td>
<td align="left">(0.044)</td>
<td align="left">[-0.107,0.085]</td>
<td align="left">[-0.095,0.074]</td>
<td align="left">[-0.084,0.085]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer</td>
<td align="right">-0.014</td>
<td align="right">0.031</td>
<td align="left">[-0.074, 0.047]</td>
<td align="right">0.004</td>
<td align="left">(0.029)</td>
<td align="left">[-0.052,0.061]</td>
<td align="right">0.005</td>
<td align="left">(0.031)</td>
<td align="left">[-0.061,0.068]</td>
<td align="left">[-0.058,0.062]</td>
<td align="left">[-0.053,0.067]</td>
</tr>
<tr class="even">
<td align="left">II. Punitive vs. Control</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">A. White vs. Black</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">0.007</td>
<td align="right">0.053</td>
<td align="left">[-0.097, 0.112]</td>
<td align="right">-0.043</td>
<td align="left">(0.048)</td>
<td align="left">[-0.136,0.051]</td>
<td align="right">-0.039</td>
<td align="left">(0.058)</td>
<td align="left">[-0.191,0.096]</td>
<td align="left">[-0.16,0.067]</td>
<td align="left">[-0.153,0.074]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback</td>
<td align="right">0.019</td>
<td align="right">0.042</td>
<td align="left">[-0.064, 0.103]</td>
<td align="right">0.030</td>
<td align="left">(0.039)</td>
<td align="left">[-0.046,0.107]</td>
<td align="right">0.027</td>
<td align="left">(0.042)</td>
<td align="left">[-0.056,0.13]</td>
<td align="left">[-0.046,0.117]</td>
<td align="left">[-0.056,0.107]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer</td>
<td align="right">0.018</td>
<td align="right">0.035</td>
<td align="left">[-0.051, 0.087]</td>
<td align="right">0.023</td>
<td align="left">(0.03)</td>
<td align="left">[-0.036,0.083]</td>
<td align="right">0.024</td>
<td align="left">(0.033)</td>
<td align="left">[-0.053,0.097]</td>
<td align="left">[-0.045,0.087]</td>
<td align="left">[-0.04,0.092]</td>
</tr>
<tr class="odd">
<td align="left">B. White vs. Hispanic</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">0.048</td>
<td align="right">0.055</td>
<td align="left">[-0.061, 0.157]</td>
<td align="right">0.023</td>
<td align="left">(0.049)</td>
<td align="left">[-0.073,0.12]</td>
<td align="right">0.023</td>
<td align="left">(0.057)</td>
<td align="left">[-0.108,0.155]</td>
<td align="left">[-0.085,0.133]</td>
<td align="left">[-0.086,0.132]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback</td>
<td align="right">-0.066</td>
<td align="right">0.041</td>
<td align="left">[-0.147, 0.015]</td>
<td align="right">-0.051</td>
<td align="left">(0.037)</td>
<td align="left">[-0.123,0.021]</td>
<td align="right">-0.056</td>
<td align="left">(0.044)</td>
<td align="left">[-0.15,0.06]</td>
<td align="left">[-0.13,0.039]</td>
<td align="left">[-0.141,0.029]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer</td>
<td align="right">-0.021</td>
<td align="right">0.034</td>
<td align="left">[-0.089, 0.046]</td>
<td align="right">0.000</td>
<td align="left">(0.031)</td>
<td align="left">[-0.061,0.061]</td>
<td align="right">0.001</td>
<td align="left">(0.035)</td>
<td align="left">[-0.078,0.076]</td>
<td align="left">[-0.069,0.068]</td>
<td align="left">[-0.068,0.069]</td>
</tr>
<tr class="odd">
<td align="left">C. Black vs. Hispanic</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">0.039</td>
<td align="right">0.049</td>
<td align="left">[-0.057, 0.134]</td>
<td align="right">0.037</td>
<td align="left">(0.045)</td>
<td align="left">[-0.052,0.125]</td>
<td align="right">0.035</td>
<td align="left">(0.05)</td>
<td align="left">[-0.073,0.15]</td>
<td align="left">[-0.059,0.138]</td>
<td align="left">[-0.065,0.132]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback</td>
<td align="right">-0.085</td>
<td align="right">0.041</td>
<td align="left">[-0.165, -0.005]</td>
<td align="right">-0.100</td>
<td align="left">(0.038)</td>
<td align="left">[-0.175,-0.025]</td>
<td align="right">-0.099</td>
<td align="left">(0.044)</td>
<td align="left">[-0.202,0.004]</td>
<td align="left">[-0.186,-0.012]</td>
<td align="left">[-0.188,-0.014]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer</td>
<td align="right">-0.039</td>
<td align="right">0.033</td>
<td align="left">[-0.105, 0.027]</td>
<td align="right">-0.032</td>
<td align="left">(0.031)</td>
<td align="left">[-0.093,0.03]</td>
<td align="right">-0.034</td>
<td align="left">(0.034)</td>
<td align="left">[-0.105,0.048]</td>
<td align="left">[-0.096,0.039]</td>
<td align="left">[-0.102,0.033]</td>
</tr>
<tr class="odd">
<td align="left">III. Punitive vs. Monitoring</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">A. White vs. Black</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">0.018</td>
<td align="right">0.058</td>
<td align="left">[-0.095, 0.132]</td>
<td align="right">-0.030</td>
<td align="left">(0.058)</td>
<td align="left">[-0.145,0.085]</td>
<td align="right">-0.026</td>
<td align="left">(0.058)</td>
<td align="left">[-0.146,0.081]</td>
<td align="left">[-0.145,0.08]</td>
<td align="left">[-0.14,0.085]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="right">0.033</td>
<td align="right">0.049</td>
<td align="left">[-0.064, 0.129]</td>
<td align="right">0.038</td>
<td align="left">(0.045)</td>
<td align="left">[-0.051,0.127]</td>
<td align="right">0.036</td>
<td align="left">(0.05)</td>
<td align="left">[-0.07,0.149]</td>
<td align="left">[-0.056,0.137]</td>
<td align="left">[-0.061,0.133]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer</td>
<td align="right">0.026</td>
<td align="right">0.037</td>
<td align="left">[-0.048, 0.099]</td>
<td align="right">0.029</td>
<td align="left">(0.034)</td>
<td align="left">[-0.037,0.095]</td>
<td align="right">0.029</td>
<td align="left">(0.034)</td>
<td align="left">[-0.041,0.1]</td>
<td align="left">[-0.038,0.097]</td>
<td align="left">[-0.038,0.097]</td>
</tr>
<tr class="even">
<td align="left">B. White vs. Hispanic</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">0.107</td>
<td align="right">0.063</td>
<td align="left">[-0.017, 0.231]</td>
<td align="right">0.082</td>
<td align="left">(0.061)</td>
<td align="left">[-0.038,0.201]</td>
<td align="right">0.081</td>
<td align="left">(0.058)</td>
<td align="left">[-0.031,0.198]</td>
<td align="left">[-0.035,0.197]</td>
<td align="left">[-0.034,0.198]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="right">-0.019</td>
<td align="right">0.049</td>
<td align="left">[-0.116, 0.078]</td>
<td align="right">-0.061</td>
<td align="left">(0.049)</td>
<td align="left">[-0.157,0.036]</td>
<td align="right">-0.059</td>
<td align="left">(0.049)</td>
<td align="left">[-0.162,0.04]</td>
<td align="left">[-0.158,0.037]</td>
<td align="left">[-0.158,0.037]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer</td>
<td align="right">0.002</td>
<td align="right">0.038</td>
<td align="left">[-0.073, 0.077]</td>
<td align="right">-0.015</td>
<td align="left">(0.035)</td>
<td align="left">[-0.084,0.055]</td>
<td align="right">-0.013</td>
<td align="left">(0.038)</td>
<td align="left">[-0.099,0.066]</td>
<td align="left">[-0.092,0.058]</td>
<td align="left">[-0.088,0.063]</td>
</tr>
<tr class="even">
<td align="left">C. Black vs. Hispanic</td>
<td align="right"></td>
<td align="right"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="right"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions</td>
<td align="right">0.139</td>
<td align="right">0.057</td>
<td align="left">[0.026, 0.251]</td>
<td align="right">0.141</td>
<td align="left">(0.06)</td>
<td align="left">[0.023,0.26]</td>
<td align="right">0.137</td>
<td align="left">(0.056)</td>
<td align="left">[0.042,0.252]</td>
<td align="left">[0.035,0.255]</td>
<td align="left">[0.028,0.247]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback</td>
<td align="right">-0.052</td>
<td align="right">0.047</td>
<td align="left">[-0.144, 0.04]</td>
<td align="right">-0.080</td>
<td align="left">(0.043)</td>
<td align="left">[-0.164,0.005]</td>
<td align="right">-0.077</td>
<td align="left">(0.046)</td>
<td align="left">[-0.181,0.017]</td>
<td align="left">[-0.171,0.008]</td>
<td align="left">[-0.167,0.012]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer</td>
<td align="right">-0.024</td>
<td align="right">0.036</td>
<td align="left">[-0.094, 0.046]</td>
<td align="right">-0.039</td>
<td align="left">(0.034)</td>
<td align="left">[-0.105,0.027]</td>
<td align="right">-0.038</td>
<td align="left">(0.036)</td>
<td align="left">[-0.116,0.035]</td>
<td align="left">[-0.108,0.031]</td>
<td align="left">[-0.109,0.03]</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:01 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrr|rrr|rrrrr}
##   \hline
## Outcome Measure &amp; Estimate &amp; SE &amp; 95\% CI &amp; Estimate &amp; SE &amp; 95\% CI &amp; Mean &amp; SE &amp; 95\% Studentized CI &amp; 95\% Basic CI &amp; 95\% Percentile CI \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; -0.015 &amp; 0.052 &amp; [-0.117, 0.087] &amp; -0.008 &amp; (0.045) &amp; [-0.096,0.079] &amp; -0.009 &amp; (0.054) &amp; [-0.14,0.127] &amp; [-0.114,0.1] &amp; [-0.117,0.098] \\ 
##   Received post-visit callback &amp; -0.002 &amp; 0.045 &amp; [-0.091, 0.088] &amp; -0.018 &amp; (0.042) &amp; [-0.1,0.065] &amp; -0.021 &amp; (0.05) &amp; [-0.133,0.102] &amp; [-0.113,0.081] &amp; [-0.117,0.077] \\ 
##   Received post-visit offer &amp; -0.003 &amp; 0.036 &amp; [-0.075, 0.068] &amp; -0.015 &amp; (0.033) &amp; [-0.079,0.049] &amp; -0.016 &amp; (0.035) &amp; [-0.092,0.061] &amp; [-0.084,0.054] &amp; [-0.084,0.054] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; -0.061 &amp; 0.057 &amp; [-0.172, 0.05] &amp; -0.098 &amp; (0.051) &amp; [-0.198,0.003] &amp; -0.090 &amp; (0.06) &amp; [-0.254,0.036] &amp; [-0.222,0.019] &amp; [-0.214,0.026] \\ 
##   Received post-visit callback &amp; -0.036 &amp; 0.043 &amp; [-0.121, 0.049] &amp; -0.034 &amp; (0.042) &amp; [-0.116,0.048] &amp; -0.032 &amp; (0.046) &amp; [-0.136,0.069] &amp; [-0.124,0.057] &amp; [-0.124,0.057] \\ 
##   Received post-visit offer &amp; -0.017 &amp; 0.034 &amp; [-0.084, 0.05] &amp; -0.021 &amp; (0.032) &amp; [-0.083,0.042] &amp; -0.021 &amp; (0.035) &amp; [-0.096,0.057] &amp; [-0.089,0.049] &amp; [-0.09,0.047] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; -0.089 &amp; 0.052 &amp; [-0.192, 0.014] &amp; -0.095 &amp; (0.048) &amp; [-0.189,0] &amp; -0.086 &amp; (0.055) &amp; [-0.237,0.023] &amp; [-0.216,0.006] &amp; [-0.196,0.026] \\ 
##   Received post-visit callback &amp; -0.035 &amp; 0.042 &amp; [-0.118, 0.048] &amp; -0.005 &amp; (0.039) &amp; [-0.083,0.073] &amp; 0.000 &amp; (0.044) &amp; [-0.107,0.085] &amp; [-0.095,0.074] &amp; [-0.084,0.085] \\ 
##   Received post-visit offer &amp; -0.014 &amp; 0.031 &amp; [-0.074, 0.047] &amp; 0.004 &amp; (0.029) &amp; [-0.052,0.061] &amp; 0.005 &amp; (0.031) &amp; [-0.061,0.068] &amp; [-0.058,0.062] &amp; [-0.053,0.067] \\ 
##   II. Punitive vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; 0.007 &amp; 0.053 &amp; [-0.097, 0.112] &amp; -0.043 &amp; (0.048) &amp; [-0.136,0.051] &amp; -0.039 &amp; (0.058) &amp; [-0.191,0.096] &amp; [-0.16,0.067] &amp; [-0.153,0.074] \\ 
##   Received post-visit callback &amp; 0.019 &amp; 0.042 &amp; [-0.064, 0.103] &amp; 0.030 &amp; (0.039) &amp; [-0.046,0.107] &amp; 0.027 &amp; (0.042) &amp; [-0.056,0.13] &amp; [-0.046,0.117] &amp; [-0.056,0.107] \\ 
##   Received post-visit offer &amp; 0.018 &amp; 0.035 &amp; [-0.051, 0.087] &amp; 0.023 &amp; (0.03) &amp; [-0.036,0.083] &amp; 0.024 &amp; (0.033) &amp; [-0.053,0.097] &amp; [-0.045,0.087] &amp; [-0.04,0.092] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; 0.048 &amp; 0.055 &amp; [-0.061, 0.157] &amp; 0.023 &amp; (0.049) &amp; [-0.073,0.12] &amp; 0.023 &amp; (0.057) &amp; [-0.108,0.155] &amp; [-0.085,0.133] &amp; [-0.086,0.132] \\ 
##   Received post-visit callback &amp; -0.066 &amp; 0.041 &amp; [-0.147, 0.015] &amp; -0.051 &amp; (0.037) &amp; [-0.123,0.021] &amp; -0.056 &amp; (0.044) &amp; [-0.15,0.06] &amp; [-0.13,0.039] &amp; [-0.141,0.029] \\ 
##   Received post-visit offer &amp; -0.021 &amp; 0.034 &amp; [-0.089, 0.046] &amp; 0.000 &amp; (0.031) &amp; [-0.061,0.061] &amp; 0.001 &amp; (0.035) &amp; [-0.078,0.076] &amp; [-0.069,0.068] &amp; [-0.068,0.069] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; 0.039 &amp; 0.049 &amp; [-0.057, 0.134] &amp; 0.037 &amp; (0.045) &amp; [-0.052,0.125] &amp; 0.035 &amp; (0.05) &amp; [-0.073,0.15] &amp; [-0.059,0.138] &amp; [-0.065,0.132] \\ 
##   Received post-visit callback &amp; -0.085 &amp; 0.041 &amp; [-0.165, -0.005] &amp; -0.100 &amp; (0.038) &amp; [-0.175,-0.025] &amp; -0.099 &amp; (0.044) &amp; [-0.202,0.004] &amp; [-0.186,-0.012] &amp; [-0.188,-0.014] \\ 
##   Received post-visit offer &amp; -0.039 &amp; 0.033 &amp; [-0.105, 0.027] &amp; -0.032 &amp; (0.031) &amp; [-0.093,0.03] &amp; -0.034 &amp; (0.034) &amp; [-0.105,0.048] &amp; [-0.096,0.039] &amp; [-0.102,0.033] \\ 
##   III. Punitive vs. Monitoring &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; 0.018 &amp; 0.058 &amp; [-0.095, 0.132] &amp; -0.030 &amp; (0.058) &amp; [-0.145,0.085] &amp; -0.026 &amp; (0.058) &amp; [-0.146,0.081] &amp; [-0.145,0.08] &amp; [-0.14,0.085] \\ 
##   Received post-visit callback &amp; 0.033 &amp; 0.049 &amp; [-0.064, 0.129] &amp; 0.038 &amp; (0.045) &amp; [-0.051,0.127] &amp; 0.036 &amp; (0.05) &amp; [-0.07,0.149] &amp; [-0.056,0.137] &amp; [-0.061,0.133] \\ 
##   Received post-visit offer &amp; 0.026 &amp; 0.037 &amp; [-0.048, 0.099] &amp; 0.029 &amp; (0.034) &amp; [-0.037,0.095] &amp; 0.029 &amp; (0.034) &amp; [-0.041,0.1] &amp; [-0.038,0.097] &amp; [-0.038,0.097] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; 0.107 &amp; 0.063 &amp; [-0.017, 0.231] &amp; 0.082 &amp; (0.061) &amp; [-0.038,0.201] &amp; 0.081 &amp; (0.058) &amp; [-0.031,0.198] &amp; [-0.035,0.197] &amp; [-0.034,0.198] \\ 
##   Received post-visit callback &amp; -0.019 &amp; 0.049 &amp; [-0.116, 0.078] &amp; -0.061 &amp; (0.049) &amp; [-0.157,0.036] &amp; -0.059 &amp; (0.049) &amp; [-0.162,0.04] &amp; [-0.158,0.037] &amp; [-0.158,0.037] \\ 
##   Received post-visit offer &amp; 0.002 &amp; 0.038 &amp; [-0.073, 0.077] &amp; -0.015 &amp; (0.035) &amp; [-0.084,0.055] &amp; -0.013 &amp; (0.038) &amp; [-0.099,0.066] &amp; [-0.092,0.058] &amp; [-0.088,0.063] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions &amp; 0.139 &amp; 0.057 &amp; [0.026, 0.251] &amp; 0.141 &amp; (0.06) &amp; [0.023,0.26] &amp; 0.137 &amp; (0.056) &amp; [0.042,0.252] &amp; [0.035,0.255] &amp; [0.028,0.247] \\ 
##   Received post-visit callback &amp; -0.052 &amp; 0.047 &amp; [-0.144, 0.04] &amp; -0.080 &amp; (0.043) &amp; [-0.164,0.005] &amp; -0.077 &amp; (0.046) &amp; [-0.181,0.017] &amp; [-0.171,0.008] &amp; [-0.167,0.012] \\ 
##   Received post-visit offer &amp; -0.024 &amp; 0.036 &amp; [-0.094, 0.046] &amp; -0.039 &amp; (0.034) &amp; [-0.105,0.027] &amp; -0.038 &amp; (0.036) &amp; [-0.116,0.035] &amp; [-0.108,0.031] &amp; [-0.109,0.03] \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
</div>
<div id="table-a16-p.a-37-missingness-analysis-estimated-correlation-between-treatment-assignment-and-missingness-on-subject-index-measure" class="section level2">
<h2><span class="header-section-number">2.19</span> Table A16 (p. A-37): Missingness Analysis: Estimated Correlation between Treatment Assignment and Missingness on Subject Index Measure</h2>
<pre class="r"><code># Missingness analysis - Part 1: Does treatment predict missingness on index net discrim measure?
# Regression analysis with block FE and IPW

miss.index &lt;- list(wb.10 = lm(is.na(index.wb) ~ TA1 + as.factor(block), data=dat[dat$TA %in% c(0,1),], weights=ipw10),
                   wb.20 = lm(is.na(index.wb) ~ TA2 + as.factor(block), data=dat[dat$TA %in% c(0,2),], weights=ipw20),
                   wb.21 = lm(is.na(index.wb) ~ TA2 + as.factor(block), data=dat[dat$TA %in% c(1,2),], weights=ipw21),
                   wh.10 = lm(is.na(index.wh) ~ TA1 + as.factor(block), data=dat[dat$TA %in% c(0,1),], weights=ipw10),
                   wh.20 = lm(is.na(index.wh) ~ TA2 + as.factor(block), data=dat[dat$TA %in% c(0,2),], weights=ipw20),
                   wh.21 = lm(is.na(index.wh) ~ TA2 + as.factor(block), data=dat[dat$TA %in% c(1,2),], weights=ipw21),
                   bh.10 = lm(is.na(index.bh) ~ TA1 + as.factor(block), data=dat[dat$TA %in% c(0,1),], weights=ipw10),
                   bh.20 = lm(is.na(index.bh) ~ TA2 + as.factor(block), data=dat[dat$TA %in% c(0,2),], weights=ipw20),
                   bh.21 = lm(is.na(index.bh) ~ TA2 + as.factor(block), data=dat[dat$TA %in% c(1,2),], weights=ipw21))

miss.index.out &lt;- cbind(do.call(rbind, lapply(miss.index, function(x) summary(x)$coefficient[2,])), # estimates
                        unlist(lapply(miss.index, function(x) summary(x)$fstatistic[1])), # f test, f statistic
                        unlist(lapply(miss.index, lmp))) # f-test p value

miss.index.out &lt;- apply(miss.index.out, 2, function(x) round(x, 3))

miss.index.out &lt;- cbind(rep(c(&quot;Monitoring vs. Control&quot;, &quot;Punitive vs. Control&quot;, &quot;Punitive vs. Monitoring&quot;), 3),
                        miss.index.out)
colnames(miss.index.out) &lt;- c(&quot;Comparison&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;t&quot;, &quot;p-value&quot;, &quot;F-statistic&quot;, &quot;F-test p-value&quot;)
rownames(miss.index.out) &lt;- NULL

out.miss &lt;- rbind(c(&quot;A. White vs. Black&quot;, rep(NA, 6)),
                  miss.index.out[1:3,],
                  c(&quot;B. White vs. Hispanic&quot;, rep(NA, 6)),
                  miss.index.out[4:6,],
                  c(&quot;C. Black vs. Hispanic&quot;, rep(NA, 6)),
                  miss.index.out[7:9,])

kable(out.miss, caption=&quot;**Table A16**&quot;)</code></pre>
<table>
<caption><strong>Table A16</strong></caption>
<thead>
<tr class="header">
<th align="left">Comparison</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">F-statistic</th>
<th align="left">F-test p-value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">A. White vs. Black</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Monitoring vs. Control</td>
<td align="left">0.073</td>
<td align="left">0.042</td>
<td align="left">1.729</td>
<td align="left">0.085</td>
<td align="left">1.481</td>
<td align="left">0.097</td>
</tr>
<tr class="odd">
<td align="left">Punitive vs. Control</td>
<td align="left">0.036</td>
<td align="left">0.04</td>
<td align="left">0.897</td>
<td align="left">0.37</td>
<td align="left">1.263</td>
<td align="left">0.212</td>
</tr>
<tr class="even">
<td align="left">Punitive vs. Monitoring</td>
<td align="left">-0.021</td>
<td align="left">0.048</td>
<td align="left">-0.436</td>
<td align="left">0.663</td>
<td align="left">0.936</td>
<td align="left">0.532</td>
</tr>
<tr class="odd">
<td align="left">B. White vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Monitoring vs. Control</td>
<td align="left">0.019</td>
<td align="left">0.044</td>
<td align="left">0.428</td>
<td align="left">0.669</td>
<td align="left">1.081</td>
<td align="left">0.369</td>
</tr>
<tr class="odd">
<td align="left">Punitive vs. Control</td>
<td align="left">-0.023</td>
<td align="left">0.04</td>
<td align="left">-0.573</td>
<td align="left">0.567</td>
<td align="left">2.187</td>
<td align="left">0.004</td>
</tr>
<tr class="even">
<td align="left">Punitive vs. Monitoring</td>
<td align="left">-0.045</td>
<td align="left">0.047</td>
<td align="left">-0.944</td>
<td align="left">0.346</td>
<td align="left">1.293</td>
<td align="left">0.194</td>
</tr>
<tr class="odd">
<td align="left">C. Black vs. Hispanic</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Monitoring vs. Control</td>
<td align="left">0.083</td>
<td align="left">0.043</td>
<td align="left">1.913</td>
<td align="left">0.056</td>
<td align="left">1.651</td>
<td align="left">0.049</td>
</tr>
<tr class="odd">
<td align="left">Punitive vs. Control</td>
<td align="left">0.038</td>
<td align="left">0.042</td>
<td align="left">0.903</td>
<td align="left">0.367</td>
<td align="left">1.247</td>
<td align="left">0.224</td>
</tr>
<tr class="even">
<td align="left">Punitive vs. Monitoring</td>
<td align="left">-0.042</td>
<td align="left">0.05</td>
<td align="left">-0.854</td>
<td align="left">0.394</td>
<td align="left">1.091</td>
<td align="left">0.36</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:01 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrrr}
##   \hline
## Comparison &amp; Estimate &amp; SE &amp; t &amp; p-value &amp; F-statistic &amp; F-test p-value \\ 
##   \hline
## A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Monitoring vs. Control &amp; 0.073 &amp; 0.042 &amp; 1.729 &amp; 0.085 &amp; 1.481 &amp; 0.097 \\ 
##   Punitive vs. Control &amp; 0.036 &amp; 0.04 &amp; 0.897 &amp; 0.37 &amp; 1.263 &amp; 0.212 \\ 
##   Punitive vs. Monitoring &amp; -0.021 &amp; 0.048 &amp; -0.436 &amp; 0.663 &amp; 0.936 &amp; 0.532 \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Monitoring vs. Control &amp; 0.019 &amp; 0.044 &amp; 0.428 &amp; 0.669 &amp; 1.081 &amp; 0.369 \\ 
##   Punitive vs. Control &amp; -0.023 &amp; 0.04 &amp; -0.573 &amp; 0.567 &amp; 2.187 &amp; 0.004 \\ 
##   Punitive vs. Monitoring &amp; -0.045 &amp; 0.047 &amp; -0.944 &amp; 0.346 &amp; 1.293 &amp; 0.194 \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Monitoring vs. Control &amp; 0.083 &amp; 0.043 &amp; 1.913 &amp; 0.056 &amp; 1.651 &amp; 0.049 \\ 
##   Punitive vs. Control &amp; 0.038 &amp; 0.042 &amp; 0.903 &amp; 0.367 &amp; 1.247 &amp; 0.224 \\ 
##   Punitive vs. Monitoring &amp; -0.042 &amp; 0.05 &amp; -0.854 &amp; 0.394 &amp; 1.091 &amp; 0.36 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a17-p.a-37-missingness-analysis-pairwise-correlations-between-missing-subjective-index-measures-and-objective-net-discrimination-measures" class="section level2">
<h2><span class="header-section-number">2.20</span> Table A17 (p. A-37): Missingness Analysis: Pairwise Correlations between Missing Subjective Index Measures and Objective Net Discrimination Measures</h2>
<pre class="r"><code># Part 2: Is missingness correlated with offers or callbacks?

miss.cor &lt;- cor(cbind(dat[,c(outvars.wb[3:4], outvars.wh[3:4], outvars.bh[3:4])], is.na(dat$index.wb), is.na(dat$index.wh), is.na(dat$index.bh)))

miss.cor &lt;- miss.cor[7:9,1:6]

miss.cor &lt;- apply(miss.cor, 2, function(x) round(x,2))
rownames(miss.cor) &lt;- paste(&quot;Missing Index Measure&quot;, c(&quot;W-B&quot;, &quot;W-H&quot;, &quot;B-H&quot;), sep=&quot;, &quot;)
miss.cor &lt;- rbind(rep(c(&quot;Callbacks&quot;, &quot;Offers&quot;), 3), miss.cor)
colnames(miss.cor) &lt;- c(&quot;W-B&quot;, &quot;W-B&quot;, &quot;W-H&quot;, &quot;W-H&quot;, &quot;B-H&quot;, &quot;B-H&quot;) # label for tex output

# output for Rmd file only (omit first row of tex table and relabel column names)
kable(miss.cor[-1,], col.names = c(&quot;W-B Callbacks&quot;, &quot;W-B Offers&quot;, &quot;W-H Callbacks&quot;, &quot;W-H Offers&quot;, &quot;B-H Callbacks&quot;, &quot;B-H Offers&quot;), caption=&quot;**Table A17**&quot;)</code></pre>
<table>
<caption><strong>Table A17</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="left">W-B Callbacks</th>
<th align="left">W-B Offers</th>
<th align="left">W-H Callbacks</th>
<th align="left">W-H Offers</th>
<th align="left">B-H Callbacks</th>
<th align="left">B-H Offers</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Missing Index Measure, W-B</td>
<td align="left">-0.05</td>
<td align="left">-0.06</td>
<td align="left">-0.08</td>
<td align="left">-0.11</td>
<td align="left">-0.02</td>
<td align="left">-0.05</td>
</tr>
<tr class="even">
<td>Missing Index Measure, W-H</td>
<td align="left">-0.11</td>
<td align="left">-0.05</td>
<td align="left">-0.02</td>
<td align="left">0</td>
<td align="left">0.09</td>
<td align="left">0.06</td>
</tr>
<tr class="odd">
<td>Missing Index Measure, B-H</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0.01</td>
<td align="left">0</td>
<td align="left">0.02</td>
<td align="left">0</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:01 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{l|cc|cc|cc}
##   \hline
##  &amp; W-B &amp; W-B &amp; W-H &amp; W-H &amp; B-H &amp; B-H \\ 
##   \hline
##  &amp; Callbacks &amp; Offers &amp; Callbacks &amp; Offers &amp; Callbacks &amp; Offers \\ 
##   Missing Index Measure, W-B &amp; -0.05 &amp; -0.06 &amp; -0.08 &amp; -0.11 &amp; -0.02 &amp; -0.05 \\ 
##   Missing Index Measure, W-H &amp; -0.11 &amp; -0.05 &amp; -0.02 &amp; 0 &amp; 0.09 &amp; 0.06 \\ 
##   Missing Index Measure, B-H &amp; 0 &amp; 0 &amp; 0.01 &amp; 0 &amp; 0.02 &amp; 0 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a18-p.a-37-missingness-analysis-predicting-missingness-on-subjective-indicators-as-a-function-of-tester-race" class="section level2">
<h2><span class="header-section-number">2.21</span> Table A18 (p. A-37): Missingness Analysis: Predicting Missingness on Subjective Indicators As a Function of Tester Race</h2>
<pre class="r"><code># Part 3: Missingness as a function of tester race/ethnicity

miss.Ys &lt;- c(&quot;sales&quot;, &quot;qualpraise&quot;, &quot;posbg&quot;, &quot;posedit&quot;, &quot;prof&quot;)
coefs &lt;- fstat &lt;- fp &lt;- list()
for(i in 1:length(miss.Ys)){
  fmla &lt;- paste0(&quot;is.na(&quot;,miss.Ys[i],&quot;) ~ ttype&quot;)
  fit &lt;- lm(formula=fmla, data=ctx)
  coefs[[i]] &lt;- apply(summary(fit)$coefficients, 2, function(x) round(x,3))
  fstat[[i]] &lt;- summary(fit)$fstatistic[1]
  fp[[i]] &lt;- lmp(fit)
}

# for subjective measures used to make index - results same across items (keep element #3)
miss3 &lt;- rbind(cbind(rownames(coefs[[1]]), coefs[[1]]),
               c(&quot;F-statistic:&quot;, round(fstat[[i]], 3), rep(NA, 3)),
               c(&quot;F test p-value:&quot;, round(fp[[1]], 3), rep(NA, 3)))
miss3[,1] &lt;- gsub(&quot;ttypeB&quot;, &quot;Hispanic tester&quot;, miss3[,1], fixed=TRUE)
miss3[,1] &lt;- gsub(&quot;ttypeC&quot;, &quot;White tester&quot;, miss3[,1], fixed=TRUE)
rownames(miss3) &lt;- NULL
colnames(miss3) &lt;- c(&quot;Variable&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;t&quot;, &quot;p-value&quot;)

kable(miss3, caption=&quot;**Table A18**&quot;)</code></pre>
<table>
<caption><strong>Table A18</strong></caption>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">(Intercept)</td>
<td align="left">0.143</td>
<td align="left">0.014</td>
<td align="left">10.267</td>
<td align="left">0</td>
</tr>
<tr class="even">
<td align="left">Hispanic tester</td>
<td align="left">-0.006</td>
<td align="left">0.02</td>
<td align="left">-0.324</td>
<td align="left">0.746</td>
</tr>
<tr class="odd">
<td align="left">White tester</td>
<td align="left">-0.008</td>
<td align="left">0.02</td>
<td align="left">-0.387</td>
<td align="left">0.699</td>
</tr>
<tr class="even">
<td align="left">F-statistic:</td>
<td align="left">0.086</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">F test p-value:</td>
<td align="left">0.917</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:01 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lcccc}
##   \hline
## Variable &amp; Estimate &amp; SE &amp; t &amp; p-value \\ 
##   \hline
## (Intercept) &amp; 0.143 &amp; 0.014 &amp; 10.267 &amp; 0 \\ 
##   Hispanic tester &amp; -0.006 &amp; 0.02 &amp; -0.324 &amp; 0.746 \\ 
##   White tester &amp; -0.008 &amp; 0.02 &amp; -0.387 &amp; 0.699 \\ 
##   F-statistic: &amp; 0.086 &amp;  &amp;  &amp;  \\ 
##   F test p-value: &amp; 0.917 &amp;  &amp;  &amp;  \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a19-p.a-38-balance-table-for-categorical-covariates" class="section level2">
<h2><span class="header-section-number">2.22</span> Table A19 (p. A-38): Balance Table for Categorical Covariates</h2>
<pre class="r"><code># CREATE BALANCE TABLES (Both categorical and continuous variables)
# unweighted, all arms
bt_unwtd = balanceTables(d=dat, z=&quot;TA&quot;, xcon=Xs.num, xcat=Xs.cat)
# weighted, all arms
bt_wtd = balanceTables(d=dat, z=&quot;TA&quot;, xcon=Xs.num, xcat=Xs.cat, weight=&quot;ipw&quot;)

# BALANCE TABLES: CATEGORICAL VARIABLES ONLY
bt_xcat = cbind(bt_unwtd$bt_cat[,1:4],
                bt_wtd$bt_cat[,2:4],
                bt_unwtd$bt_cat[,5:7],
                bt_wtd$bt_cat[,5:7],
                bt_unwtd$bt_cat[,8:10],
                bt_wtd$bt_cat[,8:10])
bt_xcat[,1] = c(&quot;Frame&quot;, &quot;Likely discrimination&quot;, &quot;Representative&quot;, &quot;Household size&quot;, &quot;1&quot;, &quot;2&quot;, &quot;Tester team gender&quot;, &quot;Female&quot;, &quot;Male&quot;)

kable(bt_xcat[,c(1:7)], 
      caption=&quot;**Table A19, Left Panel: Control Group**&quot;,
      col.names=c(&quot;Variable&quot;, &quot;Unweighted Proportion&quot;, &quot;Unweighted SE&quot;, &quot;Unweighted N&quot;,
                  &quot;Weighted Proportion&quot;, &quot;Weighted SE&quot;, &quot;Weighted N&quot;))</code></pre>
<table>
<caption><strong>Table A19, Left Panel: Control Group</strong></caption>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="left">Unweighted Proportion</th>
<th align="left">Unweighted SE</th>
<th align="left">Unweighted N</th>
<th align="left">Weighted Proportion</th>
<th align="left">Weighted SE</th>
<th align="left">Weighted N</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Frame</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Likely discrimination</td>
<td align="left">0.068</td>
<td align="left">0.015</td>
<td align="left">19</td>
<td align="left">0.07</td>
<td align="left">0.015</td>
<td align="left">46</td>
</tr>
<tr class="odd">
<td align="left">Representative</td>
<td align="left">0.932</td>
<td align="left">0.015</td>
<td align="left">260</td>
<td align="left">0.93</td>
<td align="left">0.015</td>
<td align="left">610</td>
</tr>
<tr class="even">
<td align="left">Household size</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">1</td>
<td align="left">0.774</td>
<td align="left">0.025</td>
<td align="left">216</td>
<td align="left">0.771</td>
<td align="left">0.025</td>
<td align="left">506</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">0.226</td>
<td align="left">0.025</td>
<td align="left">63</td>
<td align="left">0.229</td>
<td align="left">0.025</td>
<td align="left">150</td>
</tr>
<tr class="odd">
<td align="left">Tester team gender</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Female</td>
<td align="left">0.505</td>
<td align="left">0.03</td>
<td align="left">141</td>
<td align="left">0.514</td>
<td align="left">0.03</td>
<td align="left">337</td>
</tr>
<tr class="odd">
<td align="left">Male</td>
<td align="left">0.495</td>
<td align="left">0.03</td>
<td align="left">138</td>
<td align="left">0.486</td>
<td align="left">0.03</td>
<td align="left">319</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(bt_xcat[,c(1, 8:13)], 
      caption=&quot;**Table A19, Middle Panel: Monitoring Group**&quot;,
      col.names=c(&quot;Variable&quot;, &quot;Unweighted Proportion&quot;, &quot;Unweighted SE&quot;, &quot;Unweighted N&quot;,
                  &quot;Weighted Proportion&quot;, &quot;Weighted SE&quot;, &quot;Weighted N&quot;))</code></pre>
<table>
<caption><strong>Table A19, Middle Panel: Monitoring Group</strong></caption>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="left">Unweighted Proportion</th>
<th align="left">Unweighted SE</th>
<th align="left">Unweighted N</th>
<th align="left">Weighted Proportion</th>
<th align="left">Weighted SE</th>
<th align="left">Weighted N</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Frame</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Likely discrimination</td>
<td align="left">0.069</td>
<td align="left">0.019</td>
<td align="left">12</td>
<td align="left">0.066</td>
<td align="left">0.019</td>
<td align="left">40</td>
</tr>
<tr class="odd">
<td align="left">Representative</td>
<td align="left">0.931</td>
<td align="left">0.019</td>
<td align="left">162</td>
<td align="left">0.934</td>
<td align="left">0.019</td>
<td align="left">564</td>
</tr>
<tr class="even">
<td align="left">Household size</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">1</td>
<td align="left">0.816</td>
<td align="left">0.029</td>
<td align="left">142</td>
<td align="left">0.823</td>
<td align="left">0.029</td>
<td align="left">497</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">0.184</td>
<td align="left">0.029</td>
<td align="left">32</td>
<td align="left">0.177</td>
<td align="left">0.029</td>
<td align="left">107</td>
</tr>
<tr class="odd">
<td align="left">Tester team gender</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Female</td>
<td align="left">0.54</td>
<td align="left">0.038</td>
<td align="left">94</td>
<td align="left">0.545</td>
<td align="left">0.038</td>
<td align="left">329</td>
</tr>
<tr class="odd">
<td align="left">Male</td>
<td align="left">0.46</td>
<td align="left">0.038</td>
<td align="left">80</td>
<td align="left">0.455</td>
<td align="left">0.038</td>
<td align="left">275</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(bt_xcat[,c(1, 14:19)], 
      caption=&quot;**Table A19, Right Panel: Punitive Group**&quot;,
      col.names=c(&quot;Variable&quot;, &quot;Unweighted Proportion&quot;, &quot;Unweighted SE&quot;, &quot;Unweighted N&quot;,
                  &quot;Weighted Proportion&quot;, &quot;Weighted SE&quot;, &quot;Weighted N&quot;))</code></pre>
<table>
<caption><strong>Table A19, Right Panel: Punitive Group</strong></caption>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="left">Unweighted Proportion</th>
<th align="left">Unweighted SE</th>
<th align="left">Unweighted N</th>
<th align="left">Weighted Proportion</th>
<th align="left">Weighted SE</th>
<th align="left">Weighted N</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Frame</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Likely discrimination</td>
<td align="left">0.065</td>
<td align="left">0.017</td>
<td align="left">13</td>
<td align="left">0.063</td>
<td align="left">0.017</td>
<td align="left">43</td>
</tr>
<tr class="odd">
<td align="left">Representative</td>
<td align="left">0.935</td>
<td align="left">0.017</td>
<td align="left">187</td>
<td align="left">0.937</td>
<td align="left">0.017</td>
<td align="left">641</td>
</tr>
<tr class="even">
<td align="left">Household size</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">1</td>
<td align="left">0.795</td>
<td align="left">0.029</td>
<td align="left">159</td>
<td align="left">0.794</td>
<td align="left">0.029</td>
<td align="left">543</td>
</tr>
<tr class="even">
<td align="left">2</td>
<td align="left">0.205</td>
<td align="left">0.029</td>
<td align="left">41</td>
<td align="left">0.206</td>
<td align="left">0.029</td>
<td align="left">141</td>
</tr>
<tr class="odd">
<td align="left">Tester team gender</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Female</td>
<td align="left">0.525</td>
<td align="left">0.035</td>
<td align="left">105</td>
<td align="left">0.525</td>
<td align="left">0.035</td>
<td align="left">359</td>
</tr>
<tr class="odd">
<td align="left">Male</td>
<td align="left">0.475</td>
<td align="left">0.035</td>
<td align="left">95</td>
<td align="left">0.475</td>
<td align="left">0.035</td>
<td align="left">325</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:07 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lccc|ccc|ccc|ccc|ccc|ccc}
##   \hline
## Variable &amp; Pct.z0 &amp; SE.z0 &amp; N.z0 &amp; Pct.z0 &amp; SE.z0 &amp; Wtd N.z0 &amp; Pct.z1 &amp; SE.z1 &amp; N.z1 &amp; Pct.z1 &amp; SE.z1 &amp; Wtd N.z1 &amp; Pct.z2 &amp; SE.z2 &amp; N.z2 &amp; Pct.z2 &amp; SE.z2 &amp; Wtd N.z2 \\ 
##   \hline
## Frame &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Likely discrimination &amp; 0.068 &amp; 0.015 &amp; 19 &amp; 0.07 &amp; 0.015 &amp; 46 &amp; 0.069 &amp; 0.019 &amp; 12 &amp; 0.066 &amp; 0.019 &amp; 40 &amp; 0.065 &amp; 0.017 &amp; 13 &amp; 0.063 &amp; 0.017 &amp; 43 \\ 
##   Representative &amp; 0.932 &amp; 0.015 &amp; 260 &amp; 0.93 &amp; 0.015 &amp; 610 &amp; 0.931 &amp; 0.019 &amp; 162 &amp; 0.934 &amp; 0.019 &amp; 564 &amp; 0.935 &amp; 0.017 &amp; 187 &amp; 0.937 &amp; 0.017 &amp; 641 \\ 
##   Household size &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   1 &amp; 0.774 &amp; 0.025 &amp; 216 &amp; 0.771 &amp; 0.025 &amp; 506 &amp; 0.816 &amp; 0.029 &amp; 142 &amp; 0.823 &amp; 0.029 &amp; 497 &amp; 0.795 &amp; 0.029 &amp; 159 &amp; 0.794 &amp; 0.029 &amp; 543 \\ 
##   2 &amp; 0.226 &amp; 0.025 &amp; 63 &amp; 0.229 &amp; 0.025 &amp; 150 &amp; 0.184 &amp; 0.029 &amp; 32 &amp; 0.177 &amp; 0.029 &amp; 107 &amp; 0.205 &amp; 0.029 &amp; 41 &amp; 0.206 &amp; 0.029 &amp; 141 \\ 
##   Tester team gender &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Female &amp; 0.505 &amp; 0.03 &amp; 141 &amp; 0.514 &amp; 0.03 &amp; 337 &amp; 0.54 &amp; 0.038 &amp; 94 &amp; 0.545 &amp; 0.038 &amp; 329 &amp; 0.525 &amp; 0.035 &amp; 105 &amp; 0.525 &amp; 0.035 &amp; 359 \\ 
##   Male &amp; 0.495 &amp; 0.03 &amp; 138 &amp; 0.486 &amp; 0.03 &amp; 319 &amp; 0.46 &amp; 0.038 &amp; 80 &amp; 0.455 &amp; 0.038 &amp; 275 &amp; 0.475 &amp; 0.035 &amp; 95 &amp; 0.475 &amp; 0.035 &amp; 325 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a20-p.a-38-to-a-44-balance-table-for-continuous-covariates" class="section level2">
<h2><span class="header-section-number">2.23</span> Table A20 (p. A-38 to A-44): Balance Table for Continuous Covariates</h2>
<pre class="r"><code>bt_xcon = cbind(bt_unwtd$bt_con[,1:2],
                bt_wtd$bt_con[,2],
                bt_unwtd$bt_con[,3],
                bt_wtd$bt_con[,3],
                bt_unwtd$bt_con[,4],
                bt_wtd$bt_con[,4])

# reorder table output by covariate group
xcon_groups = list(Apartment.Characteristics = c(1:3, 53:57),
                   Randomization.Regime = 4:6,
                   Early.Stage.Discrimination = 7:39,
                   Subject.Characteristics = c(40, 61:69),
                   Tester.Call.Order = 41:43,
                   Testers.Assumed.Income = c(44:52, 58:60),
                   Tester.Fixed.Effects = 70:118)

con_tab = list()
for(i in 1:length(xcon_groups)){
  vals = xcon_groups[[i]]
  temp = list()
  for(j in 1:length(vals)){
    temp[[j]] = bt_xcon[c(3*vals[j]-2, 3*vals[j]-1, 3*vals[j]),]
  }
  temp = do.call(rbind, temp)
  temp = rbind(c(gsub(&quot;.&quot;, &quot; &quot;, names(xcon_groups)[i], fixed=TRUE), rep(NA, ncol(temp)-1)), temp)
  names(temp)[3:7] = c(&quot;Z=0 wt&quot;, &quot;Z=1&quot;, &quot;Z=1 wt&quot;, &quot;Z=2&quot;, &quot;Z=2 wt&quot;)
  con_tab[[i]] = temp
}
con_tab = do.call(rbind, con_tab)
rownames(con_tab) = NULL
colnames(con_tab) &lt;-  c(&quot;Variable&quot;, &quot;Control-Unweighted&quot;, &quot;Control-Weighted&quot;, &quot;Monitoring-Unweighted&quot;,&quot;Monitoring-Weighted&quot;, &quot;Punitive-Unweighted&quot;, &quot;Punitive-Weighted&quot;)

con_tab_labs = Xs.num
con_tab_labs = gsub(&quot;tid.&quot;, &quot;Tester ID &quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;numbr&quot;, &quot;Advertised number of bedrooms&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;o_rent&quot;, &quot;Advertised monthly rental price&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;o_sqft&quot;, &quot;Advertised square footage&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;regime&quot;, &quot;Regime &quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;boro.brx&quot;, &quot;Borough: Bronx&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;boro.brk&quot;, &quot;Borough: Brooklyn&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;boro.mnh&quot;, &quot;Borough: Manhattan&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;boro.que&quot;, &quot;Borough: Queens&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;boro.stn&quot;, &quot;Borough: Staten Island&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.wb.gt&quot;, &quot;Assumed Tester Incomes: White &gt; Black&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.wh.gt&quot;, &quot;Assumed Tester Incomes: White &gt; Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.bh.gt&quot;, &quot;Assumed Tester Incomes: Black &gt; Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.wb.eq&quot;, &quot;Assumed Tester Incomes: White = Black&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.wh.eq&quot;, &quot;Assumed Tester Incomes: White = Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.bh.eq&quot;, &quot;Assumed Tester Incomes: Black = Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.wb.lt&quot;, &quot;Assumed Tester Incomes: White &lt; Black&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.wh.lt&quot;, &quot;Assumed Tester Incomes: White &lt; Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;incrank.bh.lt&quot;, &quot;Assumed Tester Incomes: Black &lt; Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;inc.w.hi&quot;, &quot;Assumed Tester Incomes: White Highest&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;inc.b.hi&quot;, &quot;Assumed Tester Incomes: Black Highest&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;inc.h.hi&quot;, &quot;Assumed Tester Incomes: Hispanic Highest&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_api&quot;, &quot;Modal Perception of Landlord Race among Testers: Asian&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_blk&quot;, &quot;Modal Perception of Landlord Race among Testers: Black&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_hsp&quot;, &quot;Modal Perception of Landlord Race among Testers: Hispanic/Latino&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_wht&quot;, &quot;Modal Perception of Landlord Race among Testers: White&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_age_18to34&quot;, &quot;Modal Perception of Landlord Age among Testers: 18 to 34&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_age_35to44&quot;, &quot;Modal Perception of Landlord Age among Testers: 35 to 44&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_age_45to64&quot;, &quot;Modal Perception of Landlord Age among Testers: 45 to 64&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_age_65over&quot;, &quot;Modal Perception of Landlord Age among Testers: 65 and up&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;primary_age_unknown&quot;, &quot;Modal Perception of Landlord Age among Testers: Unknown/No Consensus&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;callorder.wb&quot;, &quot;Randomized Tester Call Order: White before Black&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;callorder.wh&quot;, &quot;Randomized Tester Call Order: White before Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;callorder.bh&quot;, &quot;Randomized Tester Call Order: Black before Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;broker&quot;, &quot;Subject is a Broker&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;_wb&quot;, &quot;: White-Black&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;_wh&quot;, &quot;: White-Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;_bh&quot;, &quot;: Black-Hispanic&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nnumattr&quot;, &quot;Net Diff. in Num. Attributes Inquired About over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nnumskep&quot;, &quot;Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;npctskep&quot;, &quot;Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nnumpos&quot;, &quot;Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nnumneu&quot;, &quot;Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nnumneg&quot;, &quot;Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;npctpos&quot;, &quot;Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;npctneu&quot;, &quot;Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;npctneg&quot;, &quot;Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nanyskep&quot;, &quot;Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone&quot;, con_tab_labs, fixed=TRUE)
con_tab_labs = gsub(&quot;nanyneg&quot;, &quot;Net Diff. in Receiving Any Negative Reaction to Attributes over Phone&quot;, con_tab_labs, fixed=TRUE)

for(i in 1:length(Xs.num)){
  indpos=which(con_tab[,1]==Xs.num[i])
  con_tab[indpos,1] = con_tab_labs[i]
}

# produce version to display w/ kable() -- remove all rows w/ all NAs that are included for tex formatting
con_tab_rmd &lt;- con_tab[apply(con_tab, 1, function(J) sum(is.na(J)) != ncol(con_tab) ),]
rownames(con_tab_rmd) &lt;- NULL
kable(con_tab_rmd, caption=&quot;**Table A20**&quot;)</code></pre>
<table>
<caption><strong>Table A20</strong></caption>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="left">Control-Unweighted</th>
<th align="left">Control-Weighted</th>
<th align="left">Monitoring-Unweighted</th>
<th align="left">Monitoring-Weighted</th>
<th align="left">Punitive-Unweighted</th>
<th align="left">Punitive-Weighted</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Apartment Characteristics</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Advertised number of bedrooms</td>
<td align="left">0.925</td>
<td align="left">0.928</td>
<td align="left">0.764</td>
<td align="left">0.755</td>
<td align="left">0.93</td>
<td align="left">0.928</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(1.214)</td>
<td align="left">(1.218)</td>
<td align="left">(1.126)</td>
<td align="left">(1.11)</td>
<td align="left">(1.201)</td>
<td align="left">(1.218)</td>
</tr>
<tr class="even">
<td align="left">Advertised monthly rental price</td>
<td align="left">2456.261</td>
<td align="left">2450.424</td>
<td align="left">2430.397</td>
<td align="left">2403.453</td>
<td align="left">2395.57</td>
<td align="left">2392.987</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(1271.726)</td>
<td align="left">(1230.046)</td>
<td align="left">(1224.187)</td>
<td align="left">(1209.332)</td>
<td align="left">(1115.931)</td>
<td align="left">(1116.732)</td>
</tr>
<tr class="even">
<td align="left">Advertised square footage</td>
<td align="left">1116.677</td>
<td align="left">1124.844</td>
<td align="left">836.364</td>
<td align="left">856</td>
<td align="left">960.526</td>
<td align="left">992.508</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(430.927)</td>
<td align="left">(449.448)</td>
<td align="left">(331.52)</td>
<td align="left">(336.665)</td>
<td align="left">(508.235)</td>
<td align="left">(527.196)</td>
</tr>
<tr class="even">
<td align="left">Borough: Bronx</td>
<td align="left">0.115</td>
<td align="left">0.114</td>
<td align="left">0.103</td>
<td align="left">0.108</td>
<td align="left">0.105</td>
<td align="left">0.108</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.319)</td>
<td align="left">(0.319)</td>
<td align="left">(0.305)</td>
<td align="left">(0.311)</td>
<td align="left">(0.307)</td>
<td align="left">(0.311)</td>
</tr>
<tr class="even">
<td align="left">Borough: Brooklyn</td>
<td align="left">0.358</td>
<td align="left">0.351</td>
<td align="left">0.379</td>
<td align="left">0.373</td>
<td align="left">0.34</td>
<td align="left">0.345</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.48)</td>
<td align="left">(0.478)</td>
<td align="left">(0.487)</td>
<td align="left">(0.485)</td>
<td align="left">(0.475)</td>
<td align="left">(0.477)</td>
</tr>
<tr class="even">
<td align="left">Borough: Manhattan</td>
<td align="left">0.341</td>
<td align="left">0.352</td>
<td align="left">0.368</td>
<td align="left">0.371</td>
<td align="left">0.345</td>
<td align="left">0.338</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.475)</td>
<td align="left">(0.479)</td>
<td align="left">(0.484)</td>
<td align="left">(0.484)</td>
<td align="left">(0.477)</td>
<td align="left">(0.474)</td>
</tr>
<tr class="even">
<td align="left">Borough: Queens</td>
<td align="left">0.14</td>
<td align="left">0.136</td>
<td align="left">0.115</td>
<td align="left">0.116</td>
<td align="left">0.18</td>
<td align="left">0.178</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.347)</td>
<td align="left">(0.343)</td>
<td align="left">(0.32)</td>
<td align="left">(0.321)</td>
<td align="left">(0.385)</td>
<td align="left">(0.384)</td>
</tr>
<tr class="even">
<td align="left">Borough: Staten Island</td>
<td align="left">0.047</td>
<td align="left">0.047</td>
<td align="left">0.034</td>
<td align="left">0.033</td>
<td align="left">0.03</td>
<td align="left">0.031</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.211)</td>
<td align="left">(0.213)</td>
<td align="left">(0.183)</td>
<td align="left">(0.179)</td>
<td align="left">(0.171)</td>
<td align="left">(0.173)</td>
</tr>
<tr class="even">
<td align="left">Randomization Regime</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Regime 1</td>
<td align="left">0.151</td>
<td align="left">0.192</td>
<td align="left">0.218</td>
<td align="left">0.189</td>
<td align="left">0.26</td>
<td align="left">0.228</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.358)</td>
<td align="left">(0.395)</td>
<td align="left">(0.414)</td>
<td align="left">(0.392)</td>
<td align="left">(0.44)</td>
<td align="left">(0.421)</td>
</tr>
<tr class="odd">
<td align="left">Regime 2</td>
<td align="left">0.649</td>
<td align="left">0.552</td>
<td align="left">0.471</td>
<td align="left">0.543</td>
<td align="left">0.42</td>
<td align="left">0.491</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.478)</td>
<td align="left">(0.498)</td>
<td align="left">(0.501)</td>
<td align="left">(0.5)</td>
<td align="left">(0.495)</td>
<td align="left">(0.501)</td>
</tr>
<tr class="odd">
<td align="left">Regime 3</td>
<td align="left">0.201</td>
<td align="left">0.256</td>
<td align="left">0.31</td>
<td align="left">0.268</td>
<td align="left">0.32</td>
<td align="left">0.281</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.401)</td>
<td align="left">(0.437)</td>
<td align="left">(0.464)</td>
<td align="left">(0.444)</td>
<td align="left">(0.468)</td>
<td align="left">(0.45)</td>
</tr>
<tr class="odd">
<td align="left">Early Stage Discrimination</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Inquired About over Phone: Black-Hispanic</td>
<td align="left">-0.029</td>
<td align="left">-0.07</td>
<td align="left">-0.305</td>
<td align="left">-0.272</td>
<td align="left">-0.14</td>
<td align="left">-0.099</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(2.018)</td>
<td align="left">(2.091)</td>
<td align="left">(2.218)</td>
<td align="left">(2.222)</td>
<td align="left">(2.299)</td>
<td align="left">(2.27)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Inquired About over Phone: White-Hispanic</td>
<td align="left">0.161</td>
<td align="left">0.102</td>
<td align="left">-0.149</td>
<td align="left">-0.124</td>
<td align="left">-0.255</td>
<td align="left">-0.235</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(2.051)</td>
<td align="left">(2.077)</td>
<td align="left">(2.204)</td>
<td align="left">(2.179)</td>
<td align="left">(2.018)</td>
<td align="left">(2.012)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Inquired About over Phone: White-Black</td>
<td align="left">0.19</td>
<td align="left">0.172</td>
<td align="left">0.155</td>
<td align="left">0.147</td>
<td align="left">-0.115</td>
<td align="left">-0.136</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(2.17)</td>
<td align="left">(2.172)</td>
<td align="left">(2.362)</td>
<td align="left">(2.327)</td>
<td align="left">(2.446)</td>
<td align="left">(2.418)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone: Black-Hispanic</td>
<td align="left">0</td>
<td align="left">0.008</td>
<td align="left">-0.063</td>
<td align="left">-0.063</td>
<td align="left">-0.075</td>
<td align="left">-0.067</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.416)</td>
<td align="left">(0.448)</td>
<td align="left">(0.506)</td>
<td align="left">(0.519)</td>
<td align="left">(0.609)</td>
<td align="left">(0.58)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone: White-Hispanic</td>
<td align="left">0.057</td>
<td align="left">0.047</td>
<td align="left">-0.04</td>
<td align="left">-0.036</td>
<td align="left">-0.06</td>
<td align="left">-0.044</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.533)</td>
<td align="left">(0.515)</td>
<td align="left">(0.448)</td>
<td align="left">(0.453)</td>
<td align="left">(0.639)</td>
<td align="left">(0.631)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone: White-Black</td>
<td align="left">0.057</td>
<td align="left">0.04</td>
<td align="left">0.023</td>
<td align="left">0.026</td>
<td align="left">0.015</td>
<td align="left">0.023</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.591)</td>
<td align="left">(0.605)</td>
<td align="left">(0.416)</td>
<td align="left">(0.439)</td>
<td align="left">(0.431)</td>
<td align="left">(0.443)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone: Black-Hispanic</td>
<td align="left">-0.001</td>
<td align="left">-0.001</td>
<td align="left">0.001</td>
<td align="left">0.003</td>
<td align="left">-0.025</td>
<td align="left">-0.025</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.119)</td>
<td align="left">(0.121)</td>
<td align="left">(0.202)</td>
<td align="left">(0.21)</td>
<td align="left">(0.161)</td>
<td align="left">(0.161)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone: White-Hispanic</td>
<td align="left">0.01</td>
<td align="left">0.007</td>
<td align="left">-0.015</td>
<td align="left">-0.014</td>
<td align="left">-0.018</td>
<td align="left">-0.016</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.139)</td>
<td align="left">(0.135)</td>
<td align="left">(0.128)</td>
<td align="left">(0.128)</td>
<td align="left">(0.179)</td>
<td align="left">(0.18)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone: White-Black</td>
<td align="left">0.012</td>
<td align="left">0.008</td>
<td align="left">-0.016</td>
<td align="left">-0.017</td>
<td align="left">0.007</td>
<td align="left">0.008</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.143)</td>
<td align="left">(0.142)</td>
<td align="left">(0.177)</td>
<td align="left">(0.187)</td>
<td align="left">(0.111)</td>
<td align="left">(0.11)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone: Black-Hispanic</td>
<td align="left">0.018</td>
<td align="left">0.04</td>
<td align="left">-0.006</td>
<td align="left">-0.018</td>
<td align="left">-0.065</td>
<td align="left">-0.063</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(1.03)</td>
<td align="left">(1.104)</td>
<td align="left">(1.023)</td>
<td align="left">(0.98)</td>
<td align="left">(1.252)</td>
<td align="left">(1.188)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone: White-Hispanic</td>
<td align="left">0.029</td>
<td align="left">0.02</td>
<td align="left">0.017</td>
<td align="left">0.035</td>
<td align="left">-0.08</td>
<td align="left">-0.06</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.768)</td>
<td align="left">(0.775)</td>
<td align="left">(0.909)</td>
<td align="left">(0.909)</td>
<td align="left">(1.118)</td>
<td align="left">(1.075)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone: White-Black</td>
<td align="left">0.011</td>
<td align="left">-0.02</td>
<td align="left">0.023</td>
<td align="left">0.053</td>
<td align="left">-0.015</td>
<td align="left">0.003</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.961)</td>
<td align="left">(1.031)</td>
<td align="left">(0.918)</td>
<td align="left">(0.907)</td>
<td align="left">(1.123)</td>
<td align="left">(1.088)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone: Black-Hispanic</td>
<td align="left">-0.043</td>
<td align="left">-0.11</td>
<td align="left">-0.241</td>
<td align="left">-0.192</td>
<td align="left">0</td>
<td align="left">0.037</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(1.883)</td>
<td align="left">(1.939)</td>
<td align="left">(2.126)</td>
<td align="left">(2.127)</td>
<td align="left">(2.136)</td>
<td align="left">(2.114)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone: White-Hispanic</td>
<td align="left">0.093</td>
<td align="left">0.044</td>
<td align="left">-0.132</td>
<td align="left">-0.124</td>
<td align="left">-0.17</td>
<td align="left">-0.175</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(1.783)</td>
<td align="left">(1.793)</td>
<td align="left">(2.131)</td>
<td align="left">(2.098)</td>
<td align="left">(1.854)</td>
<td align="left">(1.824)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone: White-Black</td>
<td align="left">0.136</td>
<td align="left">0.154</td>
<td align="left">0.109</td>
<td align="left">0.068</td>
<td align="left">-0.17</td>
<td align="left">-0.212</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(2.013)</td>
<td align="left">(2.016)</td>
<td align="left">(2.231)</td>
<td align="left">(2.217)</td>
<td align="left">(2.233)</td>
<td align="left">(2.204)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone: Black-Hispanic</td>
<td align="left">-0.004</td>
<td align="left">0</td>
<td align="left">-0.057</td>
<td align="left">-0.061</td>
<td align="left">-0.075</td>
<td align="left">-0.073</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.216)</td>
<td align="left">(0.228)</td>
<td align="left">(0.368)</td>
<td align="left">(0.371)</td>
<td align="left">(0.387)</td>
<td align="left">(0.372)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone: White-Hispanic</td>
<td align="left">0.039</td>
<td align="left">0.038</td>
<td align="left">-0.034</td>
<td align="left">-0.035</td>
<td align="left">-0.005</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.363)</td>
<td align="left">(0.363)</td>
<td align="left">(0.386)</td>
<td align="left">(0.392)</td>
<td align="left">(0.496)</td>
<td align="left">(0.5)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone: White-Black</td>
<td align="left">0.043</td>
<td align="left">0.038</td>
<td align="left">0.023</td>
<td align="left">0.026</td>
<td align="left">0.07</td>
<td align="left">0.073</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.386)</td>
<td align="left">(0.403)</td>
<td align="left">(0.284)</td>
<td align="left">(0.299)</td>
<td align="left">(0.382)</td>
<td align="left">(0.395)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone: Black-Hispanic</td>
<td align="left">-0.003</td>
<td align="left">0.005</td>
<td align="left">0.02</td>
<td align="left">0.014</td>
<td align="left">-0.038</td>
<td align="left">-0.034</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.268)</td>
<td align="left">(0.283)</td>
<td align="left">(0.325)</td>
<td align="left">(0.312)</td>
<td align="left">(0.279)</td>
<td align="left">(0.273)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone: White-Hispanic</td>
<td align="left">0.002</td>
<td align="left">0.001</td>
<td align="left">0.009</td>
<td align="left">0.011</td>
<td align="left">-0.032</td>
<td align="left">-0.027</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.226)</td>
<td align="left">(0.226)</td>
<td align="left">(0.266)</td>
<td align="left">(0.263)</td>
<td align="left">(0.296)</td>
<td align="left">(0.285)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone: White-Black</td>
<td align="left">0.005</td>
<td align="left">-0.003</td>
<td align="left">-0.012</td>
<td align="left">-0.004</td>
<td align="left">0.006</td>
<td align="left">0.007</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.263)</td>
<td align="left">(0.282)</td>
<td align="left">(0.292)</td>
<td align="left">(0.281)</td>
<td align="left">(0.251)</td>
<td align="left">(0.248)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone: Black-Hispanic</td>
<td align="left">-0.067</td>
<td align="left">-0.077</td>
<td align="left">-0.099</td>
<td align="left">-0.077</td>
<td align="left">-0.044</td>
<td align="left">-0.046</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.533)</td>
<td align="left">(0.546)</td>
<td align="left">(0.654)</td>
<td align="left">(0.643)</td>
<td align="left">(0.611)</td>
<td align="left">(0.597)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone: White-Hispanic</td>
<td align="left">0.011</td>
<td align="left">0.011</td>
<td align="left">-0.048</td>
<td align="left">-0.045</td>
<td align="left">-0.002</td>
<td align="left">-0.015</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.483)</td>
<td align="left">(0.488)</td>
<td align="left">(0.546)</td>
<td align="left">(0.548)</td>
<td align="left">(0.466)</td>
<td align="left">(0.458)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone: White-Black</td>
<td align="left">0.078</td>
<td align="left">0.089</td>
<td align="left">0.05</td>
<td align="left">0.033</td>
<td align="left">0.043</td>
<td align="left">0.031</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.592)</td>
<td align="left">(0.604)</td>
<td align="left">(0.616)</td>
<td align="left">(0.612)</td>
<td align="left">(0.615)</td>
<td align="left">(0.612)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone: Black-Hispanic</td>
<td align="left">-0.005</td>
<td align="left">-0.004</td>
<td align="left">-0.013</td>
<td align="left">-0.015</td>
<td align="left">-0.023</td>
<td align="left">-0.024</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.073)</td>
<td align="left">(0.071)</td>
<td align="left">(0.106)</td>
<td align="left">(0.107)</td>
<td align="left">(0.123)</td>
<td align="left">(0.128)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone: White-Hispanic</td>
<td align="left">0.009</td>
<td align="left">0.009</td>
<td align="left">-0.012</td>
<td align="left">-0.013</td>
<td align="left">-0.007</td>
<td align="left">-0.008</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.113)</td>
<td align="left">(0.109)</td>
<td align="left">(0.093)</td>
<td align="left">(0.094)</td>
<td align="left">(0.151)</td>
<td align="left">(0.154)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone: White-Black</td>
<td align="left">0.013</td>
<td align="left">0.012</td>
<td align="left">0.001</td>
<td align="left">0.002</td>
<td align="left">0.016</td>
<td align="left">0.016</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.098)</td>
<td align="left">(0.099)</td>
<td align="left">(0.062)</td>
<td align="left">(0.062)</td>
<td align="left">(0.097)</td>
<td align="left">(0.094)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone: Black-Hispanic</td>
<td align="left">-0.011</td>
<td align="left">-0.011</td>
<td align="left">-0.034</td>
<td align="left">-0.033</td>
<td align="left">-0.04</td>
<td align="left">-0.038</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.199)</td>
<td align="left">(0.21)</td>
<td align="left">(0.338)</td>
<td align="left">(0.345)</td>
<td align="left">(0.262)</td>
<td align="left">(0.257)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone: White-Hispanic</td>
<td align="left">0.025</td>
<td align="left">0.017</td>
<td align="left">-0.023</td>
<td align="left">-0.02</td>
<td align="left">-0.02</td>
<td align="left">-0.013</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.287)</td>
<td align="left">(0.284)</td>
<td align="left">(0.322)</td>
<td align="left">(0.326)</td>
<td align="left">(0.316)</td>
<td align="left">(0.323)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone: White-Black</td>
<td align="left">0.036</td>
<td align="left">0.027</td>
<td align="left">0.011</td>
<td align="left">0.013</td>
<td align="left">0.02</td>
<td align="left">0.025</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.266)</td>
<td align="left">(0.264)</td>
<td align="left">(0.304)</td>
<td align="left">(0.316)</td>
<td align="left">(0.223)</td>
<td align="left">(0.232)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Receiving Any Negative Reaction to Attributes over Phone: Black-Hispanic</td>
<td align="left">-0.007</td>
<td align="left">-0.006</td>
<td align="left">-0.034</td>
<td align="left">-0.038</td>
<td align="left">-0.055</td>
<td align="left">-0.056</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.147)</td>
<td align="left">(0.156)</td>
<td align="left">(0.238)</td>
<td align="left">(0.245)</td>
<td align="left">(0.25)</td>
<td align="left">(0.248)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Receiving Any Negative Reaction to Attributes over Phone: White-Hispanic</td>
<td align="left">0.022</td>
<td align="left">0.02</td>
<td align="left">-0.023</td>
<td align="left">-0.025</td>
<td align="left">-0.005</td>
<td align="left">-0.004</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.223)</td>
<td align="left">(0.224)</td>
<td align="left">(0.214)</td>
<td align="left">(0.218)</td>
<td align="left">(0.309)</td>
<td align="left">(0.309)</td>
</tr>
<tr class="even">
<td align="left">Net Diff. in Receiving Any Negative Reaction to Attributes over Phone: White-Black</td>
<td align="left">0.029</td>
<td align="left">0.026</td>
<td align="left">0.011</td>
<td align="left">0.013</td>
<td align="left">0.05</td>
<td align="left">0.051</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.223)</td>
<td align="left">(0.23)</td>
<td align="left">(0.186)</td>
<td align="left">(0.191)</td>
<td align="left">(0.24)</td>
<td align="left">(0.24)</td>
</tr>
<tr class="even">
<td align="left">Subject Characteristics</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Subject is a Broker</td>
<td align="left">0.86</td>
<td align="left">0.861</td>
<td align="left">0.816</td>
<td align="left">0.82</td>
<td align="left">0.85</td>
<td align="left">0.849</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.347)</td>
<td align="left">(0.346)</td>
<td align="left">(0.389)</td>
<td align="left">(0.386)</td>
<td align="left">(0.358)</td>
<td align="left">(0.359)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Race among Testers: Asian</td>
<td align="left">0.111</td>
<td align="left">0.111</td>
<td align="left">0.08</td>
<td align="left">0.079</td>
<td align="left">0.085</td>
<td align="left">0.086</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.315)</td>
<td align="left">(0.315)</td>
<td align="left">(0.273)</td>
<td align="left">(0.271)</td>
<td align="left">(0.28)</td>
<td align="left">(0.281)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Race among Testers: Black</td>
<td align="left">0.122</td>
<td align="left">0.114</td>
<td align="left">0.109</td>
<td align="left">0.109</td>
<td align="left">0.135</td>
<td align="left">0.135</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.328)</td>
<td align="left">(0.319)</td>
<td align="left">(0.313)</td>
<td align="left">(0.313)</td>
<td align="left">(0.343)</td>
<td align="left">(0.342)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Race among Testers: Hispanic/Latino</td>
<td align="left">0.158</td>
<td align="left">0.159</td>
<td align="left">0.149</td>
<td align="left">0.152</td>
<td align="left">0.12</td>
<td align="left">0.115</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.365)</td>
<td align="left">(0.366)</td>
<td align="left">(0.358)</td>
<td align="left">(0.36)</td>
<td align="left">(0.326)</td>
<td align="left">(0.32)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Race among Testers: White</td>
<td align="left">0.53</td>
<td align="left">0.537</td>
<td align="left">0.534</td>
<td align="left">0.53</td>
<td align="left">0.56</td>
<td align="left">0.56</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.5)</td>
<td align="left">(0.5)</td>
<td align="left">(0.5)</td>
<td align="left">(0.501)</td>
<td align="left">(0.498)</td>
<td align="left">(0.498)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Age among Testers: 18 to 34</td>
<td align="left">0.437</td>
<td align="left">0.441</td>
<td align="left">0.5</td>
<td align="left">0.498</td>
<td align="left">0.515</td>
<td align="left">0.512</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.497)</td>
<td align="left">(0.497)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Age among Testers: 35 to 44</td>
<td align="left">0.262</td>
<td align="left">0.265</td>
<td align="left">0.241</td>
<td align="left">0.243</td>
<td align="left">0.28</td>
<td align="left">0.285</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.44)</td>
<td align="left">(0.442)</td>
<td align="left">(0.429)</td>
<td align="left">(0.43)</td>
<td align="left">(0.45)</td>
<td align="left">(0.453)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Age among Testers: 45 to 64</td>
<td align="left">0.211</td>
<td align="left">0.207</td>
<td align="left">0.144</td>
<td align="left">0.137</td>
<td align="left">0.12</td>
<td align="left">0.117</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.409)</td>
<td align="left">(0.406)</td>
<td align="left">(0.352)</td>
<td align="left">(0.345)</td>
<td align="left">(0.326)</td>
<td align="left">(0.322)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Age among Testers: 65 and up</td>
<td align="left">0.011</td>
<td align="left">0.011</td>
<td align="left">0.017</td>
<td align="left">0.017</td>
<td align="left">0.015</td>
<td align="left">0.015</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.103)</td>
<td align="left">(0.103)</td>
<td align="left">(0.131)</td>
<td align="left">(0.128)</td>
<td align="left">(0.122)</td>
<td align="left">(0.12)</td>
</tr>
<tr class="odd">
<td align="left">Modal Perception of Landlord Age among Testers: Unknown/No Consensus</td>
<td align="left">0.079</td>
<td align="left">0.076</td>
<td align="left">0.098</td>
<td align="left">0.104</td>
<td align="left">0.07</td>
<td align="left">0.072</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.27)</td>
<td align="left">(0.266)</td>
<td align="left">(0.298)</td>
<td align="left">(0.307)</td>
<td align="left">(0.256)</td>
<td align="left">(0.259)</td>
</tr>
<tr class="odd">
<td align="left">Tester Call Order</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Randomized Tester Call Order: White before Black</td>
<td align="left">0.47</td>
<td align="left">0.48</td>
<td align="left">0.506</td>
<td align="left">0.52</td>
<td align="left">0.53</td>
<td align="left">0.537</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.5)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.5)</td>
<td align="left">(0.5)</td>
</tr>
<tr class="even">
<td align="left">Randomized Tester Call Order: White before Hispanic</td>
<td align="left">0.434</td>
<td align="left">0.434</td>
<td align="left">0.489</td>
<td align="left">0.493</td>
<td align="left">0.555</td>
<td align="left">0.554</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.496)</td>
<td align="left">(0.497)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.498)</td>
<td align="left">(0.498)</td>
</tr>
<tr class="even">
<td align="left">Randomized Tester Call Order: Black before Hispanic</td>
<td align="left">0.541</td>
<td align="left">0.544</td>
<td align="left">0.477</td>
<td align="left">0.487</td>
<td align="left">0.48</td>
<td align="left">0.48</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.499)</td>
<td align="left">(0.499)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
</tr>
<tr class="even">
<td align="left">Testers Assumed Income</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White &gt; Black</td>
<td align="left">0.466</td>
<td align="left">0.468</td>
<td align="left">0.431</td>
<td align="left">0.442</td>
<td align="left">0.45</td>
<td align="left">0.455</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.5)</td>
<td align="left">(0.5)</td>
<td align="left">(0.497)</td>
<td align="left">(0.498)</td>
<td align="left">(0.499)</td>
<td align="left">(0.499)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White &gt; Hispanic</td>
<td align="left">0.455</td>
<td align="left">0.463</td>
<td align="left">0.506</td>
<td align="left">0.508</td>
<td align="left">0.46</td>
<td align="left">0.461</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.499)</td>
<td align="left">(0.5)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.5)</td>
<td align="left">(0.5)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: Black &gt; Hispanic</td>
<td align="left">0.441</td>
<td align="left">0.445</td>
<td align="left">0.523</td>
<td align="left">0.525</td>
<td align="left">0.47</td>
<td align="left">0.472</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.497)</td>
<td align="left">(0.498)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
<td align="left">(0.5)</td>
<td align="left">(0.501)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White = Black</td>
<td align="left">0.079</td>
<td align="left">0.081</td>
<td align="left">0.098</td>
<td align="left">0.101</td>
<td align="left">0.075</td>
<td align="left">0.073</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.27)</td>
<td align="left">(0.273)</td>
<td align="left">(0.298)</td>
<td align="left">(0.302)</td>
<td align="left">(0.264)</td>
<td align="left">(0.261)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White = Hispanic</td>
<td align="left">0.082</td>
<td align="left">0.085</td>
<td align="left">0.069</td>
<td align="left">0.071</td>
<td align="left">0.07</td>
<td align="left">0.073</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.276)</td>
<td align="left">(0.28)</td>
<td align="left">(0.254)</td>
<td align="left">(0.258)</td>
<td align="left">(0.256)</td>
<td align="left">(0.261)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: Black = Hispanic</td>
<td align="left">0.154</td>
<td align="left">0.157</td>
<td align="left">0.075</td>
<td align="left">0.073</td>
<td align="left">0.08</td>
<td align="left">0.08</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.362)</td>
<td align="left">(0.364)</td>
<td align="left">(0.264)</td>
<td align="left">(0.261)</td>
<td align="left">(0.272)</td>
<td align="left">(0.273)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White &lt; Black</td>
<td align="left">0.455</td>
<td align="left">0.451</td>
<td align="left">0.471</td>
<td align="left">0.457</td>
<td align="left">0.475</td>
<td align="left">0.472</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.499)</td>
<td align="left">(0.499)</td>
<td align="left">(0.501)</td>
<td align="left">(0.5)</td>
<td align="left">(0.501)</td>
<td align="left">(0.501)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White &lt; Hispanic</td>
<td align="left">0.462</td>
<td align="left">0.451</td>
<td align="left">0.425</td>
<td align="left">0.421</td>
<td align="left">0.47</td>
<td align="left">0.466</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.499)</td>
<td align="left">(0.499)</td>
<td align="left">(0.496)</td>
<td align="left">(0.495)</td>
<td align="left">(0.5)</td>
<td align="left">(0.5)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: Black &lt; Hispanic</td>
<td align="left">0.405</td>
<td align="left">0.398</td>
<td align="left">0.402</td>
<td align="left">0.402</td>
<td align="left">0.45</td>
<td align="left">0.447</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.492)</td>
<td align="left">(0.49)</td>
<td align="left">(0.492)</td>
<td align="left">(0.492)</td>
<td align="left">(0.499)</td>
<td align="left">(0.498)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: White Highest</td>
<td align="left">0.384</td>
<td align="left">0.393</td>
<td align="left">0.385</td>
<td align="left">0.396</td>
<td align="left">0.355</td>
<td align="left">0.354</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.487)</td>
<td align="left">(0.489)</td>
<td align="left">(0.488)</td>
<td align="left">(0.49)</td>
<td align="left">(0.48)</td>
<td align="left">(0.479)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: Black Highest</td>
<td align="left">0.394</td>
<td align="left">0.39</td>
<td align="left">0.408</td>
<td align="left">0.401</td>
<td align="left">0.39</td>
<td align="left">0.392</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.49)</td>
<td align="left">(0.489)</td>
<td align="left">(0.493)</td>
<td align="left">(0.491)</td>
<td align="left">(0.489)</td>
<td align="left">(0.489)</td>
</tr>
<tr class="odd">
<td align="left">Assumed Tester Incomes: Hispanic Highest</td>
<td align="left">0.38</td>
<td align="left">0.373</td>
<td align="left">0.333</td>
<td align="left">0.329</td>
<td align="left">0.37</td>
<td align="left">0.367</td>
</tr>
<tr class="even">
<td align="left"></td>
<td align="left">(0.486)</td>
<td align="left">(0.485)</td>
<td align="left">(0.473)</td>
<td align="left">(0.471)</td>
<td align="left">(0.484)</td>
<td align="left">(0.483)</td>
</tr>
<tr class="odd">
<td align="left">Tester Fixed Effects</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Tester ID A01</td>
<td align="left">0.108</td>
<td align="left">0.11</td>
<td align="left">0.121</td>
<td align="left">0.126</td>
<td align="left">0.11</td>
<td align="left">0.111</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.31)</td>
<td align="left">(0.313)</td>
<td align="left">(0.327)</td>
<td align="left">(0.333)</td>
<td align="left">(0.314)</td>
<td align="left">(0.315)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A10</td>
<td align="left">0.136</td>
<td align="left">0.122</td>
<td align="left">0.08</td>
<td align="left">0.084</td>
<td align="left">0.11</td>
<td align="left">0.111</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.344)</td>
<td align="left">(0.328)</td>
<td align="left">(0.273)</td>
<td align="left">(0.279)</td>
<td align="left">(0.314)</td>
<td align="left">(0.315)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A11</td>
<td align="left">0.029</td>
<td align="left">0.024</td>
<td align="left">0.023</td>
<td align="left">0.026</td>
<td align="left">0.035</td>
<td align="left">0.039</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.167)</td>
<td align="left">(0.155)</td>
<td align="left">(0.15)</td>
<td align="left">(0.161)</td>
<td align="left">(0.184)</td>
<td align="left">(0.195)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A13</td>
<td align="left">0.154</td>
<td align="left">0.168</td>
<td align="left">0.144</td>
<td align="left">0.141</td>
<td align="left">0.17</td>
<td align="left">0.156</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.362)</td>
<td align="left">(0.374)</td>
<td align="left">(0.352)</td>
<td align="left">(0.349)</td>
<td align="left">(0.377)</td>
<td align="left">(0.364)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A02</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0.01</td>
<td align="left">0.009</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0.1)</td>
<td align="left">(0.093)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A21</td>
<td align="left">0.007</td>
<td align="left">0.009</td>
<td align="left">0.011</td>
<td align="left">0.01</td>
<td align="left">0.005</td>
<td align="left">0.004</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.085)</td>
<td align="left">(0.095)</td>
<td align="left">(0.107)</td>
<td align="left">(0.099)</td>
<td align="left">(0.071)</td>
<td align="left">(0.066)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A22</td>
<td align="left">0.043</td>
<td align="left">0.055</td>
<td align="left">0.121</td>
<td align="left">0.104</td>
<td align="left">0.055</td>
<td align="left">0.048</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.203)</td>
<td align="left">(0.228)</td>
<td align="left">(0.327)</td>
<td align="left">(0.307)</td>
<td align="left">(0.229)</td>
<td align="left">(0.215)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A03</td>
<td align="left">0.057</td>
<td align="left">0.061</td>
<td align="left">0.069</td>
<td align="left">0.063</td>
<td align="left">0.085</td>
<td align="left">0.085</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.233)</td>
<td align="left">(0.24)</td>
<td align="left">(0.254)</td>
<td align="left">(0.244)</td>
<td align="left">(0.28)</td>
<td align="left">(0.279)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A04</td>
<td align="left">0.086</td>
<td align="left">0.081</td>
<td align="left">0.069</td>
<td align="left">0.073</td>
<td align="left">0.1</td>
<td align="left">0.111</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.281)</td>
<td align="left">(0.273)</td>
<td align="left">(0.254)</td>
<td align="left">(0.261)</td>
<td align="left">(0.301)</td>
<td align="left">(0.315)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A05</td>
<td align="left">0.14</td>
<td align="left">0.128</td>
<td align="left">0.155</td>
<td align="left">0.164</td>
<td align="left">0.11</td>
<td align="left">0.12</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.347)</td>
<td align="left">(0.335)</td>
<td align="left">(0.363)</td>
<td align="left">(0.371)</td>
<td align="left">(0.314)</td>
<td align="left">(0.326)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A06</td>
<td align="left">0.061</td>
<td align="left">0.059</td>
<td align="left">0.052</td>
<td align="left">0.051</td>
<td align="left">0.06</td>
<td align="left">0.058</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.24)</td>
<td align="left">(0.237)</td>
<td align="left">(0.222)</td>
<td align="left">(0.221)</td>
<td align="left">(0.238)</td>
<td align="left">(0.235)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A07</td>
<td align="left">0.004</td>
<td align="left">0.005</td>
<td align="left">0.011</td>
<td align="left">0.01</td>
<td align="left">0.005</td>
<td align="left">0.004</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.06)</td>
<td align="left">(0.068)</td>
<td align="left">(0.107)</td>
<td align="left">(0.099)</td>
<td align="left">(0.071)</td>
<td align="left">(0.066)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A08</td>
<td align="left">0.168</td>
<td align="left">0.169</td>
<td align="left">0.138</td>
<td align="left">0.142</td>
<td align="left">0.14</td>
<td align="left">0.137</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.375)</td>
<td align="left">(0.376)</td>
<td align="left">(0.346)</td>
<td align="left">(0.35)</td>
<td align="left">(0.348)</td>
<td align="left">(0.345)</td>
</tr>
<tr class="even">
<td align="left">Tester ID A09</td>
<td align="left">0.007</td>
<td align="left">0.009</td>
<td align="left">0.006</td>
<td align="left">0.005</td>
<td align="left">0.005</td>
<td align="left">0.004</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.085)</td>
<td align="left">(0.095)</td>
<td align="left">(0.076)</td>
<td align="left">(0.071)</td>
<td align="left">(0.071)</td>
<td align="left">(0.066)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B01</td>
<td align="left">0.204</td>
<td align="left">0.189</td>
<td align="left">0.27</td>
<td align="left">0.291</td>
<td align="left">0.19</td>
<td align="left">0.203</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.404)</td>
<td align="left">(0.392)</td>
<td align="left">(0.445)</td>
<td align="left">(0.456)</td>
<td align="left">(0.393)</td>
<td align="left">(0.403)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B11</td>
<td align="left">0.061</td>
<td align="left">0.052</td>
<td align="left">0.029</td>
<td align="left">0.031</td>
<td align="left">0.055</td>
<td align="left">0.063</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.24)</td>
<td align="left">(0.222)</td>
<td align="left">(0.168)</td>
<td align="left">(0.175)</td>
<td align="left">(0.229)</td>
<td align="left">(0.243)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B12</td>
<td align="left">0.011</td>
<td align="left">0.009</td>
<td align="left">0.006</td>
<td align="left">0.007</td>
<td align="left">0</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.103)</td>
<td align="left">(0.095)</td>
<td align="left">(0.076)</td>
<td align="left">(0.081)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B14</td>
<td align="left">0.108</td>
<td align="left">0.116</td>
<td align="left">0.115</td>
<td align="left">0.106</td>
<td align="left">0.115</td>
<td align="left">0.115</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.31)</td>
<td align="left">(0.321)</td>
<td align="left">(0.32)</td>
<td align="left">(0.309)</td>
<td align="left">(0.32)</td>
<td align="left">(0.32)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B16</td>
<td align="left">0.032</td>
<td align="left">0.027</td>
<td align="left">0.034</td>
<td align="left">0.04</td>
<td align="left">0.035</td>
<td align="left">0.035</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.177)</td>
<td align="left">(0.164)</td>
<td align="left">(0.183)</td>
<td align="left">(0.196)</td>
<td align="left">(0.184)</td>
<td align="left">(0.184)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B02</td>
<td align="left">0.025</td>
<td align="left">0.032</td>
<td align="left">0.04</td>
<td align="left">0.035</td>
<td align="left">0.04</td>
<td align="left">0.035</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.157)</td>
<td align="left">(0.176)</td>
<td align="left">(0.197)</td>
<td align="left">(0.184)</td>
<td align="left">(0.196)</td>
<td align="left">(0.184)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B20</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0.005</td>
<td align="left">0.004</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0.071)</td>
<td align="left">(0.066)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B23</td>
<td align="left">0.014</td>
<td align="left">0.018</td>
<td align="left">0.011</td>
<td align="left">0.01</td>
<td align="left">0.03</td>
<td align="left">0.026</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.119)</td>
<td align="left">(0.134)</td>
<td align="left">(0.107)</td>
<td align="left">(0.099)</td>
<td align="left">(0.171)</td>
<td align="left">(0.16)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B24</td>
<td align="left">0.022</td>
<td align="left">0.027</td>
<td align="left">0.063</td>
<td align="left">0.055</td>
<td align="left">0.04</td>
<td align="left">0.035</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.145)</td>
<td align="left">(0.164)</td>
<td align="left">(0.244)</td>
<td align="left">(0.228)</td>
<td align="left">(0.196)</td>
<td align="left">(0.184)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B25</td>
<td align="left">0.039</td>
<td align="left">0.05</td>
<td align="left">0.057</td>
<td align="left">0.05</td>
<td align="left">0.08</td>
<td align="left">0.07</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.195)</td>
<td align="left">(0.219)</td>
<td align="left">(0.233)</td>
<td align="left">(0.218)</td>
<td align="left">(0.272)</td>
<td align="left">(0.256)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B27</td>
<td align="left">0.022</td>
<td align="left">0.027</td>
<td align="left">0.046</td>
<td align="left">0.04</td>
<td align="left">0.02</td>
<td align="left">0.018</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.145)</td>
<td align="left">(0.164)</td>
<td align="left">(0.21)</td>
<td align="left">(0.196)</td>
<td align="left">(0.14)</td>
<td align="left">(0.132)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B03</td>
<td align="left">0.104</td>
<td align="left">0.117</td>
<td align="left">0.086</td>
<td align="left">0.083</td>
<td align="left">0.095</td>
<td align="left">0.091</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.306)</td>
<td align="left">(0.322)</td>
<td align="left">(0.281)</td>
<td align="left">(0.276)</td>
<td align="left">(0.294)</td>
<td align="left">(0.288)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B04</td>
<td align="left">0.022</td>
<td align="left">0.027</td>
<td align="left">0.034</td>
<td align="left">0.03</td>
<td align="left">0.045</td>
<td align="left">0.039</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.145)</td>
<td align="left">(0.164)</td>
<td align="left">(0.183)</td>
<td align="left">(0.171)</td>
<td align="left">(0.208)</td>
<td align="left">(0.195)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B06</td>
<td align="left">0.043</td>
<td align="left">0.044</td>
<td align="left">0.04</td>
<td align="left">0.04</td>
<td align="left">0.065</td>
<td align="left">0.064</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.203)</td>
<td align="left">(0.206)</td>
<td align="left">(0.197)</td>
<td align="left">(0.196)</td>
<td align="left">(0.247)</td>
<td align="left">(0.246)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B07</td>
<td align="left">0.018</td>
<td align="left">0.023</td>
<td align="left">0.023</td>
<td align="left">0.02</td>
<td align="left">0.035</td>
<td align="left">0.031</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.133)</td>
<td align="left">(0.15)</td>
<td align="left">(0.15)</td>
<td align="left">(0.14)</td>
<td align="left">(0.184)</td>
<td align="left">(0.173)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B08</td>
<td align="left">0.233</td>
<td align="left">0.203</td>
<td align="left">0.126</td>
<td align="left">0.144</td>
<td align="left">0.13</td>
<td align="left">0.146</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.423)</td>
<td align="left">(0.403)</td>
<td align="left">(0.333)</td>
<td align="left">(0.352)</td>
<td align="left">(0.337)</td>
<td align="left">(0.354)</td>
</tr>
<tr class="even">
<td align="left">Tester ID B09</td>
<td align="left">0.043</td>
<td align="left">0.037</td>
<td align="left">0.017</td>
<td align="left">0.02</td>
<td align="left">0.02</td>
<td align="left">0.023</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.203)</td>
<td align="left">(0.188)</td>
<td align="left">(0.131)</td>
<td align="left">(0.14)</td>
<td align="left">(0.14)</td>
<td align="left">(0.152)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C01</td>
<td align="left">0.025</td>
<td align="left">0.032</td>
<td align="left">0.029</td>
<td align="left">0.025</td>
<td align="left">0.015</td>
<td align="left">0.013</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.157)</td>
<td align="left">(0.176)</td>
<td align="left">(0.168)</td>
<td align="left">(0.156)</td>
<td align="left">(0.122)</td>
<td align="left">(0.114)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C10</td>
<td align="left">0.022</td>
<td align="left">0.023</td>
<td align="left">0.046</td>
<td align="left">0.041</td>
<td align="left">0.035</td>
<td align="left">0.034</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.145)</td>
<td align="left">(0.15)</td>
<td align="left">(0.21)</td>
<td align="left">(0.2)</td>
<td align="left">(0.184)</td>
<td align="left">(0.181)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C12</td>
<td align="left">0.004</td>
<td align="left">0.003</td>
<td align="left">0.011</td>
<td align="left">0.013</td>
<td align="left">0.015</td>
<td align="left">0.015</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.06)</td>
<td align="left">(0.055)</td>
<td align="left">(0.107)</td>
<td align="left">(0.115)</td>
<td align="left">(0.122)</td>
<td align="left">(0.12)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C13</td>
<td align="left">0.097</td>
<td align="left">0.082</td>
<td align="left">0.063</td>
<td align="left">0.073</td>
<td align="left">0.045</td>
<td align="left">0.053</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.296)</td>
<td align="left">(0.275)</td>
<td align="left">(0.244)</td>
<td align="left">(0.261)</td>
<td align="left">(0.208)</td>
<td align="left">(0.224)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C14</td>
<td align="left">0.007</td>
<td align="left">0.009</td>
<td align="left">0.057</td>
<td align="left">0.05</td>
<td align="left">0.015</td>
<td align="left">0.013</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.085)</td>
<td align="left">(0.095)</td>
<td align="left">(0.233)</td>
<td align="left">(0.218)</td>
<td align="left">(0.122)</td>
<td align="left">(0.114)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C15</td>
<td align="left">0.018</td>
<td align="left">0.023</td>
<td align="left">0.052</td>
<td align="left">0.045</td>
<td align="left">0.045</td>
<td align="left">0.039</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.133)</td>
<td align="left">(0.15)</td>
<td align="left">(0.222)</td>
<td align="left">(0.207)</td>
<td align="left">(0.208)</td>
<td align="left">(0.195)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C02</td>
<td align="left">0.28</td>
<td align="left">0.276</td>
<td align="left">0.207</td>
<td align="left">0.21</td>
<td align="left">0.245</td>
<td align="left">0.249</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.45)</td>
<td align="left">(0.448)</td>
<td align="left">(0.406)</td>
<td align="left">(0.409)</td>
<td align="left">(0.431)</td>
<td align="left">(0.433)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C27</td>
<td align="left">0.039</td>
<td align="left">0.05</td>
<td align="left">0.023</td>
<td align="left">0.02</td>
<td align="left">0.07</td>
<td align="left">0.061</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.195)</td>
<td align="left">(0.219)</td>
<td align="left">(0.15)</td>
<td align="left">(0.14)</td>
<td align="left">(0.256)</td>
<td align="left">(0.241)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C29</td>
<td align="left">0.05</td>
<td align="left">0.064</td>
<td align="left">0.069</td>
<td align="left">0.06</td>
<td align="left">0.05</td>
<td align="left">0.044</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.219)</td>
<td align="left">(0.245)</td>
<td align="left">(0.254)</td>
<td align="left">(0.237)</td>
<td align="left">(0.218)</td>
<td align="left">(0.205)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C03</td>
<td align="left">0.004</td>
<td align="left">0.005</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0.03</td>
<td align="left">0.026</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.06)</td>
<td align="left">(0.068)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0.171)</td>
<td align="left">(0.16)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C31</td>
<td align="left">0.004</td>
<td align="left">0.005</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.06)</td>
<td align="left">(0.068)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C33</td>
<td align="left">0.011</td>
<td align="left">0.009</td>
<td align="left">0.006</td>
<td align="left">0.007</td>
<td align="left">0.005</td>
<td align="left">0.006</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.103)</td>
<td align="left">(0.095)</td>
<td align="left">(0.076)</td>
<td align="left">(0.081)</td>
<td align="left">(0.071)</td>
<td align="left">(0.076)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C04</td>
<td align="left">0.179</td>
<td align="left">0.171</td>
<td align="left">0.126</td>
<td align="left">0.131</td>
<td align="left">0.16</td>
<td align="left">0.161</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.384)</td>
<td align="left">(0.377)</td>
<td align="left">(0.333)</td>
<td align="left">(0.338)</td>
<td align="left">(0.368)</td>
<td align="left">(0.368)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C05</td>
<td align="left">0.011</td>
<td align="left">0.014</td>
<td align="left">0.029</td>
<td align="left">0.025</td>
<td align="left">0.04</td>
<td align="left">0.035</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.103)</td>
<td align="left">(0.117)</td>
<td align="left">(0.168)</td>
<td align="left">(0.156)</td>
<td align="left">(0.196)</td>
<td align="left">(0.184)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C06</td>
<td align="left">0.004</td>
<td align="left">0.005</td>
<td align="left">0.006</td>
<td align="left">0.005</td>
<td align="left">0.005</td>
<td align="left">0.004</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.06)</td>
<td align="left">(0.068)</td>
<td align="left">(0.076)</td>
<td align="left">(0.071)</td>
<td align="left">(0.071)</td>
<td align="left">(0.066)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C07</td>
<td align="left">0.244</td>
<td align="left">0.226</td>
<td align="left">0.253</td>
<td align="left">0.276</td>
<td align="left">0.2</td>
<td align="left">0.225</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.43)</td>
<td align="left">(0.419)</td>
<td align="left">(0.436)</td>
<td align="left">(0.449)</td>
<td align="left">(0.401)</td>
<td align="left">(0.419)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C08</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0.006</td>
<td align="left">0.005</td>
<td align="left">0</td>
<td align="left">0</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0)</td>
<td align="left">(0)</td>
<td align="left">(0.076)</td>
<td align="left">(0.071)</td>
<td align="left">(0)</td>
<td align="left">(0)</td>
</tr>
<tr class="even">
<td align="left">Tester ID C09</td>
<td align="left">0.004</td>
<td align="left">0.005</td>
<td align="left">0.017</td>
<td align="left">0.015</td>
<td align="left">0.025</td>
<td align="left">0.022</td>
</tr>
<tr class="odd">
<td align="left"></td>
<td align="left">(0.06)</td>
<td align="left">(0.068)</td>
<td align="left">(0.131)</td>
<td align="left">(0.122)</td>
<td align="left">(0.157)</td>
<td align="left">(0.147)</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:07 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lllllll}
##   \hline
## Variable &amp; Control-Unweighted &amp; Control-Weighted &amp; Monitoring-Unweighted &amp; Monitoring-Weighted &amp; Punitive-Unweighted &amp; Punitive-Weighted \\ 
##   \hline
## Apartment Characteristics &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Advertised number of bedrooms &amp; 0.925 &amp; 0.928 &amp; 0.764 &amp; 0.755 &amp; 0.93 &amp; 0.928 \\ 
##    &amp; (1.214) &amp; (1.218) &amp; (1.126) &amp; (1.11) &amp; (1.201) &amp; (1.218) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Advertised monthly rental price &amp; 2456.261 &amp; 2450.424 &amp; 2430.397 &amp; 2403.453 &amp; 2395.57 &amp; 2392.987 \\ 
##    &amp; (1271.726) &amp; (1230.046) &amp; (1224.187) &amp; (1209.332) &amp; (1115.931) &amp; (1116.732) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Advertised square footage &amp; 1116.677 &amp; 1124.844 &amp; 836.364 &amp; 856 &amp; 960.526 &amp; 992.508 \\ 
##    &amp; (430.927) &amp; (449.448) &amp; (331.52) &amp; (336.665) &amp; (508.235) &amp; (527.196) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Borough: Bronx &amp; 0.115 &amp; 0.114 &amp; 0.103 &amp; 0.108 &amp; 0.105 &amp; 0.108 \\ 
##    &amp; (0.319) &amp; (0.319) &amp; (0.305) &amp; (0.311) &amp; (0.307) &amp; (0.311) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Borough: Brooklyn &amp; 0.358 &amp; 0.351 &amp; 0.379 &amp; 0.373 &amp; 0.34 &amp; 0.345 \\ 
##    &amp; (0.48) &amp; (0.478) &amp; (0.487) &amp; (0.485) &amp; (0.475) &amp; (0.477) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Borough: Manhattan &amp; 0.341 &amp; 0.352 &amp; 0.368 &amp; 0.371 &amp; 0.345 &amp; 0.338 \\ 
##    &amp; (0.475) &amp; (0.479) &amp; (0.484) &amp; (0.484) &amp; (0.477) &amp; (0.474) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Borough: Queens &amp; 0.14 &amp; 0.136 &amp; 0.115 &amp; 0.116 &amp; 0.18 &amp; 0.178 \\ 
##    &amp; (0.347) &amp; (0.343) &amp; (0.32) &amp; (0.321) &amp; (0.385) &amp; (0.384) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Borough: Staten Island &amp; 0.047 &amp; 0.047 &amp; 0.034 &amp; 0.033 &amp; 0.03 &amp; 0.031 \\ 
##    &amp; (0.211) &amp; (0.213) &amp; (0.183) &amp; (0.179) &amp; (0.171) &amp; (0.173) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Randomization Regime &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Regime 1 &amp; 0.151 &amp; 0.192 &amp; 0.218 &amp; 0.189 &amp; 0.26 &amp; 0.228 \\ 
##    &amp; (0.358) &amp; (0.395) &amp; (0.414) &amp; (0.392) &amp; (0.44) &amp; (0.421) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Regime 2 &amp; 0.649 &amp; 0.552 &amp; 0.471 &amp; 0.543 &amp; 0.42 &amp; 0.491 \\ 
##    &amp; (0.478) &amp; (0.498) &amp; (0.501) &amp; (0.5) &amp; (0.495) &amp; (0.501) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Regime 3 &amp; 0.201 &amp; 0.256 &amp; 0.31 &amp; 0.268 &amp; 0.32 &amp; 0.281 \\ 
##    &amp; (0.401) &amp; (0.437) &amp; (0.464) &amp; (0.444) &amp; (0.468) &amp; (0.45) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Early Stage Discrimination &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Inquired About over Phone: Black-Hispanic &amp; -0.029 &amp; -0.07 &amp; -0.305 &amp; -0.272 &amp; -0.14 &amp; -0.099 \\ 
##    &amp; (2.018) &amp; (2.091) &amp; (2.218) &amp; (2.222) &amp; (2.299) &amp; (2.27) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Inquired About over Phone: White-Hispanic &amp; 0.161 &amp; 0.102 &amp; -0.149 &amp; -0.124 &amp; -0.255 &amp; -0.235 \\ 
##    &amp; (2.051) &amp; (2.077) &amp; (2.204) &amp; (2.179) &amp; (2.018) &amp; (2.012) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Inquired About over Phone: White-Black &amp; 0.19 &amp; 0.172 &amp; 0.155 &amp; 0.147 &amp; -0.115 &amp; -0.136 \\ 
##    &amp; (2.17) &amp; (2.172) &amp; (2.362) &amp; (2.327) &amp; (2.446) &amp; (2.418) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone: Black-Hispanic &amp; 0 &amp; 0.008 &amp; -0.063 &amp; -0.063 &amp; -0.075 &amp; -0.067 \\ 
##    &amp; (0.416) &amp; (0.448) &amp; (0.506) &amp; (0.519) &amp; (0.609) &amp; (0.58) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone: White-Hispanic &amp; 0.057 &amp; 0.047 &amp; -0.04 &amp; -0.036 &amp; -0.06 &amp; -0.044 \\ 
##    &amp; (0.533) &amp; (0.515) &amp; (0.448) &amp; (0.453) &amp; (0.639) &amp; (0.631) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Skeptical Reaction over Phone: White-Black &amp; 0.057 &amp; 0.04 &amp; 0.023 &amp; 0.026 &amp; 0.015 &amp; 0.023 \\ 
##    &amp; (0.591) &amp; (0.605) &amp; (0.416) &amp; (0.439) &amp; (0.431) &amp; (0.443) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone: Black-Hispanic &amp; -0.001 &amp; -0.001 &amp; 0.001 &amp; 0.003 &amp; -0.025 &amp; -0.025 \\ 
##    &amp; (0.119) &amp; (0.121) &amp; (0.202) &amp; (0.21) &amp; (0.161) &amp; (0.161) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone: White-Hispanic &amp; 0.01 &amp; 0.007 &amp; -0.015 &amp; -0.014 &amp; -0.018 &amp; -0.016 \\ 
##    &amp; (0.139) &amp; (0.135) &amp; (0.128) &amp; (0.128) &amp; (0.179) &amp; (0.18) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Skeptical Reaction over Phone: White-Black &amp; 0.012 &amp; 0.008 &amp; -0.016 &amp; -0.017 &amp; 0.007 &amp; 0.008 \\ 
##    &amp; (0.143) &amp; (0.142) &amp; (0.177) &amp; (0.187) &amp; (0.111) &amp; (0.11) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone: Black-Hispanic &amp; 0.018 &amp; 0.04 &amp; -0.006 &amp; -0.018 &amp; -0.065 &amp; -0.063 \\ 
##    &amp; (1.03) &amp; (1.104) &amp; (1.023) &amp; (0.98) &amp; (1.252) &amp; (1.188) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone: White-Hispanic &amp; 0.029 &amp; 0.02 &amp; 0.017 &amp; 0.035 &amp; -0.08 &amp; -0.06 \\ 
##    &amp; (0.768) &amp; (0.775) &amp; (0.909) &amp; (0.909) &amp; (1.118) &amp; (1.075) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Positive Reaction over Phone: White-Black &amp; 0.011 &amp; -0.02 &amp; 0.023 &amp; 0.053 &amp; -0.015 &amp; 0.003 \\ 
##    &amp; (0.961) &amp; (1.031) &amp; (0.918) &amp; (0.907) &amp; (1.123) &amp; (1.088) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone: Black-Hispanic &amp; -0.043 &amp; -0.11 &amp; -0.241 &amp; -0.192 &amp; 0 &amp; 0.037 \\ 
##    &amp; (1.883) &amp; (1.939) &amp; (2.126) &amp; (2.127) &amp; (2.136) &amp; (2.114) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone: White-Hispanic &amp; 0.093 &amp; 0.044 &amp; -0.132 &amp; -0.124 &amp; -0.17 &amp; -0.175 \\ 
##    &amp; (1.783) &amp; (1.793) &amp; (2.131) &amp; (2.098) &amp; (1.854) &amp; (1.824) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Neutral Reaction over Phone: White-Black &amp; 0.136 &amp; 0.154 &amp; 0.109 &amp; 0.068 &amp; -0.17 &amp; -0.212 \\ 
##    &amp; (2.013) &amp; (2.016) &amp; (2.231) &amp; (2.217) &amp; (2.233) &amp; (2.204) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone: Black-Hispanic &amp; -0.004 &amp; 0 &amp; -0.057 &amp; -0.061 &amp; -0.075 &amp; -0.073 \\ 
##    &amp; (0.216) &amp; (0.228) &amp; (0.368) &amp; (0.371) &amp; (0.387) &amp; (0.372) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone: White-Hispanic &amp; 0.039 &amp; 0.038 &amp; -0.034 &amp; -0.035 &amp; -0.005 &amp; 0 \\ 
##    &amp; (0.363) &amp; (0.363) &amp; (0.386) &amp; (0.392) &amp; (0.496) &amp; (0.5) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Num. Attributes Eliciting Negative Reaction over Phone: White-Black &amp; 0.043 &amp; 0.038 &amp; 0.023 &amp; 0.026 &amp; 0.07 &amp; 0.073 \\ 
##    &amp; (0.386) &amp; (0.403) &amp; (0.284) &amp; (0.299) &amp; (0.382) &amp; (0.395) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone: Black-Hispanic &amp; -0.003 &amp; 0.005 &amp; 0.02 &amp; 0.014 &amp; -0.038 &amp; -0.034 \\ 
##    &amp; (0.268) &amp; (0.283) &amp; (0.325) &amp; (0.312) &amp; (0.279) &amp; (0.273) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone: White-Hispanic &amp; 0.002 &amp; 0.001 &amp; 0.009 &amp; 0.011 &amp; -0.032 &amp; -0.027 \\ 
##    &amp; (0.226) &amp; (0.226) &amp; (0.266) &amp; (0.263) &amp; (0.296) &amp; (0.285) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Positive Reaction over Phone: White-Black &amp; 0.005 &amp; -0.003 &amp; -0.012 &amp; -0.004 &amp; 0.006 &amp; 0.007 \\ 
##    &amp; (0.263) &amp; (0.282) &amp; (0.292) &amp; (0.281) &amp; (0.251) &amp; (0.248) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone: Black-Hispanic &amp; -0.067 &amp; -0.077 &amp; -0.099 &amp; -0.077 &amp; -0.044 &amp; -0.046 \\ 
##    &amp; (0.533) &amp; (0.546) &amp; (0.654) &amp; (0.643) &amp; (0.611) &amp; (0.597) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone: White-Hispanic &amp; 0.011 &amp; 0.011 &amp; -0.048 &amp; -0.045 &amp; -0.002 &amp; -0.015 \\ 
##    &amp; (0.483) &amp; (0.488) &amp; (0.546) &amp; (0.548) &amp; (0.466) &amp; (0.458) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Neutral Reaction over Phone: White-Black &amp; 0.078 &amp; 0.089 &amp; 0.05 &amp; 0.033 &amp; 0.043 &amp; 0.031 \\ 
##    &amp; (0.592) &amp; (0.604) &amp; (0.616) &amp; (0.612) &amp; (0.615) &amp; (0.612) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone: Black-Hispanic &amp; -0.005 &amp; -0.004 &amp; -0.013 &amp; -0.015 &amp; -0.023 &amp; -0.024 \\ 
##    &amp; (0.073) &amp; (0.071) &amp; (0.106) &amp; (0.107) &amp; (0.123) &amp; (0.128) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone: White-Hispanic &amp; 0.009 &amp; 0.009 &amp; -0.012 &amp; -0.013 &amp; -0.007 &amp; -0.008 \\ 
##    &amp; (0.113) &amp; (0.109) &amp; (0.093) &amp; (0.094) &amp; (0.151) &amp; (0.154) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Pct. of Attributes Raised Eliciting Negative Reaction over Phone: White-Black &amp; 0.013 &amp; 0.012 &amp; 0.001 &amp; 0.002 &amp; 0.016 &amp; 0.016 \\ 
##    &amp; (0.098) &amp; (0.099) &amp; (0.062) &amp; (0.062) &amp; (0.097) &amp; (0.094) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone: Black-Hispanic &amp; -0.011 &amp; -0.011 &amp; -0.034 &amp; -0.033 &amp; -0.04 &amp; -0.038 \\ 
##    &amp; (0.199) &amp; (0.21) &amp; (0.338) &amp; (0.345) &amp; (0.262) &amp; (0.257) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone: White-Hispanic &amp; 0.025 &amp; 0.017 &amp; -0.023 &amp; -0.02 &amp; -0.02 &amp; -0.013 \\ 
##    &amp; (0.287) &amp; (0.284) &amp; (0.322) &amp; (0.326) &amp; (0.316) &amp; (0.323) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Receiving Any Skeptical Reaction to Attributes over Phone: White-Black &amp; 0.036 &amp; 0.027 &amp; 0.011 &amp; 0.013 &amp; 0.02 &amp; 0.025 \\ 
##    &amp; (0.266) &amp; (0.264) &amp; (0.304) &amp; (0.316) &amp; (0.223) &amp; (0.232) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Receiving Any Negative Reaction to Attributes over Phone: Black-Hispanic &amp; -0.007 &amp; -0.006 &amp; -0.034 &amp; -0.038 &amp; -0.055 &amp; -0.056 \\ 
##    &amp; (0.147) &amp; (0.156) &amp; (0.238) &amp; (0.245) &amp; (0.25) &amp; (0.248) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Receiving Any Negative Reaction to Attributes over Phone: White-Hispanic &amp; 0.022 &amp; 0.02 &amp; -0.023 &amp; -0.025 &amp; -0.005 &amp; -0.004 \\ 
##    &amp; (0.223) &amp; (0.224) &amp; (0.214) &amp; (0.218) &amp; (0.309) &amp; (0.309) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Net Diff. in Receiving Any Negative Reaction to Attributes over Phone: White-Black &amp; 0.029 &amp; 0.026 &amp; 0.011 &amp; 0.013 &amp; 0.05 &amp; 0.051 \\ 
##    &amp; (0.223) &amp; (0.23) &amp; (0.186) &amp; (0.191) &amp; (0.24) &amp; (0.24) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Subject Characteristics &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Subject is a Broker &amp; 0.86 &amp; 0.861 &amp; 0.816 &amp; 0.82 &amp; 0.85 &amp; 0.849 \\ 
##    &amp; (0.347) &amp; (0.346) &amp; (0.389) &amp; (0.386) &amp; (0.358) &amp; (0.359) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Race among Testers: Asian &amp; 0.111 &amp; 0.111 &amp; 0.08 &amp; 0.079 &amp; 0.085 &amp; 0.086 \\ 
##    &amp; (0.315) &amp; (0.315) &amp; (0.273) &amp; (0.271) &amp; (0.28) &amp; (0.281) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Race among Testers: Black &amp; 0.122 &amp; 0.114 &amp; 0.109 &amp; 0.109 &amp; 0.135 &amp; 0.135 \\ 
##    &amp; (0.328) &amp; (0.319) &amp; (0.313) &amp; (0.313) &amp; (0.343) &amp; (0.342) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Race among Testers: Hispanic/Latino &amp; 0.158 &amp; 0.159 &amp; 0.149 &amp; 0.152 &amp; 0.12 &amp; 0.115 \\ 
##    &amp; (0.365) &amp; (0.366) &amp; (0.358) &amp; (0.36) &amp; (0.326) &amp; (0.32) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Race among Testers: White &amp; 0.53 &amp; 0.537 &amp; 0.534 &amp; 0.53 &amp; 0.56 &amp; 0.56 \\ 
##    &amp; (0.5) &amp; (0.5) &amp; (0.5) &amp; (0.501) &amp; (0.498) &amp; (0.498) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Age among Testers: 18 to 34 &amp; 0.437 &amp; 0.441 &amp; 0.5 &amp; 0.498 &amp; 0.515 &amp; 0.512 \\ 
##    &amp; (0.497) &amp; (0.497) &amp; (0.501) &amp; (0.501) &amp; (0.501) &amp; (0.501) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Age among Testers: 35 to 44 &amp; 0.262 &amp; 0.265 &amp; 0.241 &amp; 0.243 &amp; 0.28 &amp; 0.285 \\ 
##    &amp; (0.44) &amp; (0.442) &amp; (0.429) &amp; (0.43) &amp; (0.45) &amp; (0.453) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Age among Testers: 45 to 64 &amp; 0.211 &amp; 0.207 &amp; 0.144 &amp; 0.137 &amp; 0.12 &amp; 0.117 \\ 
##    &amp; (0.409) &amp; (0.406) &amp; (0.352) &amp; (0.345) &amp; (0.326) &amp; (0.322) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Age among Testers: 65 and up &amp; 0.011 &amp; 0.011 &amp; 0.017 &amp; 0.017 &amp; 0.015 &amp; 0.015 \\ 
##    &amp; (0.103) &amp; (0.103) &amp; (0.131) &amp; (0.128) &amp; (0.122) &amp; (0.12) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Modal Perception of Landlord Age among Testers: Unknown/No Consensus &amp; 0.079 &amp; 0.076 &amp; 0.098 &amp; 0.104 &amp; 0.07 &amp; 0.072 \\ 
##    &amp; (0.27) &amp; (0.266) &amp; (0.298) &amp; (0.307) &amp; (0.256) &amp; (0.259) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester Call Order &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Randomized Tester Call Order: White before Black &amp; 0.47 &amp; 0.48 &amp; 0.506 &amp; 0.52 &amp; 0.53 &amp; 0.537 \\ 
##    &amp; (0.5) &amp; (0.501) &amp; (0.501) &amp; (0.501) &amp; (0.5) &amp; (0.5) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Randomized Tester Call Order: White before Hispanic &amp; 0.434 &amp; 0.434 &amp; 0.489 &amp; 0.493 &amp; 0.555 &amp; 0.554 \\ 
##    &amp; (0.496) &amp; (0.497) &amp; (0.501) &amp; (0.501) &amp; (0.498) &amp; (0.498) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Randomized Tester Call Order: Black before Hispanic &amp; 0.541 &amp; 0.544 &amp; 0.477 &amp; 0.487 &amp; 0.48 &amp; 0.48 \\ 
##    &amp; (0.499) &amp; (0.499) &amp; (0.501) &amp; (0.501) &amp; (0.501) &amp; (0.501) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Testers Assumed Income &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White $&gt;$ Black &amp; 0.466 &amp; 0.468 &amp; 0.431 &amp; 0.442 &amp; 0.45 &amp; 0.455 \\ 
##    &amp; (0.5) &amp; (0.5) &amp; (0.497) &amp; (0.498) &amp; (0.499) &amp; (0.499) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White $&gt;$ Hispanic &amp; 0.455 &amp; 0.463 &amp; 0.506 &amp; 0.508 &amp; 0.46 &amp; 0.461 \\ 
##    &amp; (0.499) &amp; (0.5) &amp; (0.501) &amp; (0.501) &amp; (0.5) &amp; (0.5) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: Black $&gt;$ Hispanic &amp; 0.441 &amp; 0.445 &amp; 0.523 &amp; 0.525 &amp; 0.47 &amp; 0.472 \\ 
##    &amp; (0.497) &amp; (0.498) &amp; (0.501) &amp; (0.501) &amp; (0.5) &amp; (0.501) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White = Black &amp; 0.079 &amp; 0.081 &amp; 0.098 &amp; 0.101 &amp; 0.075 &amp; 0.073 \\ 
##    &amp; (0.27) &amp; (0.273) &amp; (0.298) &amp; (0.302) &amp; (0.264) &amp; (0.261) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White = Hispanic &amp; 0.082 &amp; 0.085 &amp; 0.069 &amp; 0.071 &amp; 0.07 &amp; 0.073 \\ 
##    &amp; (0.276) &amp; (0.28) &amp; (0.254) &amp; (0.258) &amp; (0.256) &amp; (0.261) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: Black = Hispanic &amp; 0.154 &amp; 0.157 &amp; 0.075 &amp; 0.073 &amp; 0.08 &amp; 0.08 \\ 
##    &amp; (0.362) &amp; (0.364) &amp; (0.264) &amp; (0.261) &amp; (0.272) &amp; (0.273) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White $&lt;$ Black &amp; 0.455 &amp; 0.451 &amp; 0.471 &amp; 0.457 &amp; 0.475 &amp; 0.472 \\ 
##    &amp; (0.499) &amp; (0.499) &amp; (0.501) &amp; (0.5) &amp; (0.501) &amp; (0.501) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White $&lt;$ Hispanic &amp; 0.462 &amp; 0.451 &amp; 0.425 &amp; 0.421 &amp; 0.47 &amp; 0.466 \\ 
##    &amp; (0.499) &amp; (0.499) &amp; (0.496) &amp; (0.495) &amp; (0.5) &amp; (0.5) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: Black $&lt;$ Hispanic &amp; 0.405 &amp; 0.398 &amp; 0.402 &amp; 0.402 &amp; 0.45 &amp; 0.447 \\ 
##    &amp; (0.492) &amp; (0.49) &amp; (0.492) &amp; (0.492) &amp; (0.499) &amp; (0.498) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: White Highest &amp; 0.384 &amp; 0.393 &amp; 0.385 &amp; 0.396 &amp; 0.355 &amp; 0.354 \\ 
##    &amp; (0.487) &amp; (0.489) &amp; (0.488) &amp; (0.49) &amp; (0.48) &amp; (0.479) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: Black Highest &amp; 0.394 &amp; 0.39 &amp; 0.408 &amp; 0.401 &amp; 0.39 &amp; 0.392 \\ 
##    &amp; (0.49) &amp; (0.489) &amp; (0.493) &amp; (0.491) &amp; (0.489) &amp; (0.489) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Assumed Tester Incomes: Hispanic Highest &amp; 0.38 &amp; 0.373 &amp; 0.333 &amp; 0.329 &amp; 0.37 &amp; 0.367 \\ 
##    &amp; (0.486) &amp; (0.485) &amp; (0.473) &amp; (0.471) &amp; (0.484) &amp; (0.483) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester Fixed Effects &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A01 &amp; 0.108 &amp; 0.11 &amp; 0.121 &amp; 0.126 &amp; 0.11 &amp; 0.111 \\ 
##    &amp; (0.31) &amp; (0.313) &amp; (0.327) &amp; (0.333) &amp; (0.314) &amp; (0.315) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A10 &amp; 0.136 &amp; 0.122 &amp; 0.08 &amp; 0.084 &amp; 0.11 &amp; 0.111 \\ 
##    &amp; (0.344) &amp; (0.328) &amp; (0.273) &amp; (0.279) &amp; (0.314) &amp; (0.315) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A11 &amp; 0.029 &amp; 0.024 &amp; 0.023 &amp; 0.026 &amp; 0.035 &amp; 0.039 \\ 
##    &amp; (0.167) &amp; (0.155) &amp; (0.15) &amp; (0.161) &amp; (0.184) &amp; (0.195) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A13 &amp; 0.154 &amp; 0.168 &amp; 0.144 &amp; 0.141 &amp; 0.17 &amp; 0.156 \\ 
##    &amp; (0.362) &amp; (0.374) &amp; (0.352) &amp; (0.349) &amp; (0.377) &amp; (0.364) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A02 &amp; 0 &amp; 0 &amp; 0 &amp; 0 &amp; 0.01 &amp; 0.009 \\ 
##    &amp; (0) &amp; (0) &amp; (0) &amp; (0) &amp; (0.1) &amp; (0.093) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A21 &amp; 0.007 &amp; 0.009 &amp; 0.011 &amp; 0.01 &amp; 0.005 &amp; 0.004 \\ 
##    &amp; (0.085) &amp; (0.095) &amp; (0.107) &amp; (0.099) &amp; (0.071) &amp; (0.066) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A22 &amp; 0.043 &amp; 0.055 &amp; 0.121 &amp; 0.104 &amp; 0.055 &amp; 0.048 \\ 
##    &amp; (0.203) &amp; (0.228) &amp; (0.327) &amp; (0.307) &amp; (0.229) &amp; (0.215) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A03 &amp; 0.057 &amp; 0.061 &amp; 0.069 &amp; 0.063 &amp; 0.085 &amp; 0.085 \\ 
##    &amp; (0.233) &amp; (0.24) &amp; (0.254) &amp; (0.244) &amp; (0.28) &amp; (0.279) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A04 &amp; 0.086 &amp; 0.081 &amp; 0.069 &amp; 0.073 &amp; 0.1 &amp; 0.111 \\ 
##    &amp; (0.281) &amp; (0.273) &amp; (0.254) &amp; (0.261) &amp; (0.301) &amp; (0.315) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A05 &amp; 0.14 &amp; 0.128 &amp; 0.155 &amp; 0.164 &amp; 0.11 &amp; 0.12 \\ 
##    &amp; (0.347) &amp; (0.335) &amp; (0.363) &amp; (0.371) &amp; (0.314) &amp; (0.326) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A06 &amp; 0.061 &amp; 0.059 &amp; 0.052 &amp; 0.051 &amp; 0.06 &amp; 0.058 \\ 
##    &amp; (0.24) &amp; (0.237) &amp; (0.222) &amp; (0.221) &amp; (0.238) &amp; (0.235) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A07 &amp; 0.004 &amp; 0.005 &amp; 0.011 &amp; 0.01 &amp; 0.005 &amp; 0.004 \\ 
##    &amp; (0.06) &amp; (0.068) &amp; (0.107) &amp; (0.099) &amp; (0.071) &amp; (0.066) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A08 &amp; 0.168 &amp; 0.169 &amp; 0.138 &amp; 0.142 &amp; 0.14 &amp; 0.137 \\ 
##    &amp; (0.375) &amp; (0.376) &amp; (0.346) &amp; (0.35) &amp; (0.348) &amp; (0.345) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID A09 &amp; 0.007 &amp; 0.009 &amp; 0.006 &amp; 0.005 &amp; 0.005 &amp; 0.004 \\ 
##    &amp; (0.085) &amp; (0.095) &amp; (0.076) &amp; (0.071) &amp; (0.071) &amp; (0.066) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B01 &amp; 0.204 &amp; 0.189 &amp; 0.27 &amp; 0.291 &amp; 0.19 &amp; 0.203 \\ 
##    &amp; (0.404) &amp; (0.392) &amp; (0.445) &amp; (0.456) &amp; (0.393) &amp; (0.403) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B11 &amp; 0.061 &amp; 0.052 &amp; 0.029 &amp; 0.031 &amp; 0.055 &amp; 0.063 \\ 
##    &amp; (0.24) &amp; (0.222) &amp; (0.168) &amp; (0.175) &amp; (0.229) &amp; (0.243) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B12 &amp; 0.011 &amp; 0.009 &amp; 0.006 &amp; 0.007 &amp; 0 &amp; 0 \\ 
##    &amp; (0.103) &amp; (0.095) &amp; (0.076) &amp; (0.081) &amp; (0) &amp; (0) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B14 &amp; 0.108 &amp; 0.116 &amp; 0.115 &amp; 0.106 &amp; 0.115 &amp; 0.115 \\ 
##    &amp; (0.31) &amp; (0.321) &amp; (0.32) &amp; (0.309) &amp; (0.32) &amp; (0.32) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B16 &amp; 0.032 &amp; 0.027 &amp; 0.034 &amp; 0.04 &amp; 0.035 &amp; 0.035 \\ 
##    &amp; (0.177) &amp; (0.164) &amp; (0.183) &amp; (0.196) &amp; (0.184) &amp; (0.184) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B02 &amp; 0.025 &amp; 0.032 &amp; 0.04 &amp; 0.035 &amp; 0.04 &amp; 0.035 \\ 
##    &amp; (0.157) &amp; (0.176) &amp; (0.197) &amp; (0.184) &amp; (0.196) &amp; (0.184) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B20 &amp; 0 &amp; 0 &amp; 0 &amp; 0 &amp; 0.005 &amp; 0.004 \\ 
##    &amp; (0) &amp; (0) &amp; (0) &amp; (0) &amp; (0.071) &amp; (0.066) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B23 &amp; 0.014 &amp; 0.018 &amp; 0.011 &amp; 0.01 &amp; 0.03 &amp; 0.026 \\ 
##    &amp; (0.119) &amp; (0.134) &amp; (0.107) &amp; (0.099) &amp; (0.171) &amp; (0.16) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B24 &amp; 0.022 &amp; 0.027 &amp; 0.063 &amp; 0.055 &amp; 0.04 &amp; 0.035 \\ 
##    &amp; (0.145) &amp; (0.164) &amp; (0.244) &amp; (0.228) &amp; (0.196) &amp; (0.184) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B25 &amp; 0.039 &amp; 0.05 &amp; 0.057 &amp; 0.05 &amp; 0.08 &amp; 0.07 \\ 
##    &amp; (0.195) &amp; (0.219) &amp; (0.233) &amp; (0.218) &amp; (0.272) &amp; (0.256) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B27 &amp; 0.022 &amp; 0.027 &amp; 0.046 &amp; 0.04 &amp; 0.02 &amp; 0.018 \\ 
##    &amp; (0.145) &amp; (0.164) &amp; (0.21) &amp; (0.196) &amp; (0.14) &amp; (0.132) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B03 &amp; 0.104 &amp; 0.117 &amp; 0.086 &amp; 0.083 &amp; 0.095 &amp; 0.091 \\ 
##    &amp; (0.306) &amp; (0.322) &amp; (0.281) &amp; (0.276) &amp; (0.294) &amp; (0.288) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B04 &amp; 0.022 &amp; 0.027 &amp; 0.034 &amp; 0.03 &amp; 0.045 &amp; 0.039 \\ 
##    &amp; (0.145) &amp; (0.164) &amp; (0.183) &amp; (0.171) &amp; (0.208) &amp; (0.195) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B06 &amp; 0.043 &amp; 0.044 &amp; 0.04 &amp; 0.04 &amp; 0.065 &amp; 0.064 \\ 
##    &amp; (0.203) &amp; (0.206) &amp; (0.197) &amp; (0.196) &amp; (0.247) &amp; (0.246) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B07 &amp; 0.018 &amp; 0.023 &amp; 0.023 &amp; 0.02 &amp; 0.035 &amp; 0.031 \\ 
##    &amp; (0.133) &amp; (0.15) &amp; (0.15) &amp; (0.14) &amp; (0.184) &amp; (0.173) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B08 &amp; 0.233 &amp; 0.203 &amp; 0.126 &amp; 0.144 &amp; 0.13 &amp; 0.146 \\ 
##    &amp; (0.423) &amp; (0.403) &amp; (0.333) &amp; (0.352) &amp; (0.337) &amp; (0.354) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID B09 &amp; 0.043 &amp; 0.037 &amp; 0.017 &amp; 0.02 &amp; 0.02 &amp; 0.023 \\ 
##    &amp; (0.203) &amp; (0.188) &amp; (0.131) &amp; (0.14) &amp; (0.14) &amp; (0.152) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C01 &amp; 0.025 &amp; 0.032 &amp; 0.029 &amp; 0.025 &amp; 0.015 &amp; 0.013 \\ 
##    &amp; (0.157) &amp; (0.176) &amp; (0.168) &amp; (0.156) &amp; (0.122) &amp; (0.114) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C10 &amp; 0.022 &amp; 0.023 &amp; 0.046 &amp; 0.041 &amp; 0.035 &amp; 0.034 \\ 
##    &amp; (0.145) &amp; (0.15) &amp; (0.21) &amp; (0.2) &amp; (0.184) &amp; (0.181) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C12 &amp; 0.004 &amp; 0.003 &amp; 0.011 &amp; 0.013 &amp; 0.015 &amp; 0.015 \\ 
##    &amp; (0.06) &amp; (0.055) &amp; (0.107) &amp; (0.115) &amp; (0.122) &amp; (0.12) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C13 &amp; 0.097 &amp; 0.082 &amp; 0.063 &amp; 0.073 &amp; 0.045 &amp; 0.053 \\ 
##    &amp; (0.296) &amp; (0.275) &amp; (0.244) &amp; (0.261) &amp; (0.208) &amp; (0.224) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C14 &amp; 0.007 &amp; 0.009 &amp; 0.057 &amp; 0.05 &amp; 0.015 &amp; 0.013 \\ 
##    &amp; (0.085) &amp; (0.095) &amp; (0.233) &amp; (0.218) &amp; (0.122) &amp; (0.114) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C15 &amp; 0.018 &amp; 0.023 &amp; 0.052 &amp; 0.045 &amp; 0.045 &amp; 0.039 \\ 
##    &amp; (0.133) &amp; (0.15) &amp; (0.222) &amp; (0.207) &amp; (0.208) &amp; (0.195) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C02 &amp; 0.28 &amp; 0.276 &amp; 0.207 &amp; 0.21 &amp; 0.245 &amp; 0.249 \\ 
##    &amp; (0.45) &amp; (0.448) &amp; (0.406) &amp; (0.409) &amp; (0.431) &amp; (0.433) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C27 &amp; 0.039 &amp; 0.05 &amp; 0.023 &amp; 0.02 &amp; 0.07 &amp; 0.061 \\ 
##    &amp; (0.195) &amp; (0.219) &amp; (0.15) &amp; (0.14) &amp; (0.256) &amp; (0.241) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C29 &amp; 0.05 &amp; 0.064 &amp; 0.069 &amp; 0.06 &amp; 0.05 &amp; 0.044 \\ 
##    &amp; (0.219) &amp; (0.245) &amp; (0.254) &amp; (0.237) &amp; (0.218) &amp; (0.205) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C03 &amp; 0.004 &amp; 0.005 &amp; 0 &amp; 0 &amp; 0.03 &amp; 0.026 \\ 
##    &amp; (0.06) &amp; (0.068) &amp; (0) &amp; (0) &amp; (0.171) &amp; (0.16) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C31 &amp; 0.004 &amp; 0.005 &amp; 0 &amp; 0 &amp; 0 &amp; 0 \\ 
##    &amp; (0.06) &amp; (0.068) &amp; (0) &amp; (0) &amp; (0) &amp; (0) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C33 &amp; 0.011 &amp; 0.009 &amp; 0.006 &amp; 0.007 &amp; 0.005 &amp; 0.006 \\ 
##    &amp; (0.103) &amp; (0.095) &amp; (0.076) &amp; (0.081) &amp; (0.071) &amp; (0.076) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C04 &amp; 0.179 &amp; 0.171 &amp; 0.126 &amp; 0.131 &amp; 0.16 &amp; 0.161 \\ 
##    &amp; (0.384) &amp; (0.377) &amp; (0.333) &amp; (0.338) &amp; (0.368) &amp; (0.368) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C05 &amp; 0.011 &amp; 0.014 &amp; 0.029 &amp; 0.025 &amp; 0.04 &amp; 0.035 \\ 
##    &amp; (0.103) &amp; (0.117) &amp; (0.168) &amp; (0.156) &amp; (0.196) &amp; (0.184) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C06 &amp; 0.004 &amp; 0.005 &amp; 0.006 &amp; 0.005 &amp; 0.005 &amp; 0.004 \\ 
##    &amp; (0.06) &amp; (0.068) &amp; (0.076) &amp; (0.071) &amp; (0.071) &amp; (0.066) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C07 &amp; 0.244 &amp; 0.226 &amp; 0.253 &amp; 0.276 &amp; 0.2 &amp; 0.225 \\ 
##    &amp; (0.43) &amp; (0.419) &amp; (0.436) &amp; (0.449) &amp; (0.401) &amp; (0.419) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C08 &amp; 0 &amp; 0 &amp; 0.006 &amp; 0.005 &amp; 0 &amp; 0 \\ 
##    &amp; (0) &amp; (0) &amp; (0.076) &amp; (0.071) &amp; (0) &amp; (0) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Tester ID C09 &amp; 0.004 &amp; 0.005 &amp; 0.017 &amp; 0.015 &amp; 0.025 &amp; 0.022 \\ 
##    &amp; (0.06) &amp; (0.068) &amp; (0.131) &amp; (0.122) &amp; (0.157) &amp; (0.147) \\ 
##    &amp;  &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a21-p.a-46-sensitivity-analysis-estimated-effects-of-messaging-on-net-discrimination-levels-among-subsample-excluding-likely-discrimination-cases" class="section level2">
<h2><span class="header-section-number">2.24</span> Table A21 (p. A-46): Sensitivity Analysis: Estimated Effects of Messaging on Net Discrimination Levels Among Subsample Excluding Likely Discrimination Cases</h2>
<pre class="r"><code>col.labels &lt;- c(&quot;Outcome&quot;,c(&quot;Estimate&quot;,&quot;SE&quot;,&quot;t&quot;,&quot;P&quot;))

# ---------------------------------------------------------------------- #
# MODELS WITH ONLY BLOCK FIXED EFFECTS

itt.wb.mc &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)
itt.wb.pc &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)
itt.wb.pm &lt;- matrix(NA, nrow=length(outvars.wb), ncol=5)

itt.wh.mc &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)
itt.wh.pc &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)
itt.wh.pm &lt;- matrix(NA, nrow=length(outvars.wh), ncol=5)

itt.bh.mc &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)
itt.bh.pc &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)
itt.bh.pm &lt;- matrix(NA, nrow=length(outvars.bh), ncol=5)

fit.wb.mc &lt;- fit.wb.pc &lt;- fit.wb.pm &lt;- fit.wh.mc &lt;- fit.wh.pc &lt;- fit.wh.pm &lt;- fit.bh.mc &lt;- fit.bh.pc &lt;- fit.bh.pm &lt;- list() # store results from ITT est w/ block FE (no other covs)

for(i in 1:length(outvars.wb)){  
  
  model.mc &lt;- paste(outvars.wb[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wb[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.wb.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1) &amp; dat$frame == &quot;representative&quot;,], weights=ipw10)
  fit.wb.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2) &amp; dat$frame == &quot;representative&quot;,], weights=ipw20)
  fit.wb.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2) &amp; dat$frame == &quot;representative&quot;,], weights=ipw21)
  
  itt.wb.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.wb.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.wb.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
  
  itt.wb.mc[i,5] &lt;- pt(coef(summary(fit.mc))[,3], summary(fit.mc)$df[2], lower=TRUE)[2] #one sided p
  itt.wb.pc[i,5] &lt;- pt(coef(summary(fit.pc))[,3], summary(fit.pc)$df[2], lower=TRUE)[2] #one sided p
  
}

for(i in 1:length(outvars.wh)){  
  
  model.mc &lt;- paste(outvars.wh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.wh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.wh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1) &amp; dat$frame == &quot;representative&quot;,], weights=ipw10)
  fit.wh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2) &amp; dat$frame == &quot;representative&quot;,], weights=ipw20)
  fit.wh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2) &amp; dat$frame == &quot;representative&quot;,], weights=ipw21)
  
  itt.wh.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.wh.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.wh.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
  
  itt.wh.mc[i,5] &lt;- pt(coef(summary(fit.mc))[,3], summary(fit.mc)$df[2], lower=TRUE)[2] #one sided p
  itt.wh.pc[i,5] &lt;- pt(coef(summary(fit.pc))[,3], summary(fit.pc)$df[2], lower=TRUE)[2] #one sided p
  
}

for(i in 1:length(outvars.bh)){  
  
  model.mc &lt;- paste(outvars.bh[i],&quot; ~ TA1 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pc &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  model.pm &lt;- paste(outvars.bh[i],&quot; ~ TA2 + as.factor(block)&quot;,sep=&quot;&quot;)
  
  fit.bh.mc[[i]] &lt;- fit.mc &lt;- lm(formula=model.mc, data=dat[dat$TA %in% c(0,1) &amp; dat$frame == &quot;representative&quot;,], weights=ipw10)
  fit.bh.pc[[i]] &lt;- fit.pc &lt;- lm(formula=model.pc, data=dat[dat$TA %in% c(0,2) &amp; dat$frame == &quot;representative&quot;,], weights=ipw20)
  fit.bh.pm[[i]] &lt;- fit.pm &lt;- lm(formula=model.pm, data=dat[dat$TA %in% c(1,2) &amp; dat$frame == &quot;representative&quot;,], weights=ipw21)
  
  itt.bh.mc[i,2:5] &lt;- summary(fit.mc)$coefficients[2,]
  itt.bh.pc[i,2:5] &lt;- summary(fit.pc)$coefficients[2,]
  itt.bh.pm[i,2:5] &lt;- summary(fit.pm)$coefficients[2,]
  
}

# Add outcome measure labels
itt.wb.mc[,1] &lt;- outvars.wb.labs
itt.wb.pc[,1] &lt;- outvars.wb.labs
itt.wb.pm[,1] &lt;- outvars.wb.labs

itt.wh.mc[,1] &lt;- outvars.wh.labs
itt.wh.pc[,1] &lt;- outvars.wh.labs
itt.wh.pm[,1] &lt;- outvars.wh.labs

itt.bh.mc[,1] &lt;- outvars.bh.labs
itt.bh.pc[,1] &lt;- outvars.bh.labs
itt.bh.pm[,1] &lt;- outvars.bh.labs

# Add column names
colnames(itt.wb.mc) &lt;- colnames(itt.wb.pc) &lt;- colnames(itt.wb.pm) &lt;- col.labels
colnames(itt.wh.mc) &lt;- colnames(itt.wh.pc) &lt;- colnames(itt.wh.pm) &lt;- col.labels
colnames(itt.bh.mc) &lt;- colnames(itt.bh.pc) &lt;- colnames(itt.bh.pm) &lt;- col.labels

# Stack, create output table
out2 &lt;- rbind(itt.wb.mc, itt.wh.mc, itt.bh.mc,
              itt.wb.pc, itt.wh.pc, itt.bh.pc,
              itt.wb.pm, itt.wh.pm, itt.bh.pm)

out2 &lt;- out2[-seq(1,33,4),]

#----- CIs -----#

df.wb.mc &lt;- unlist(lapply(fit.wb.mc, function(x) summary(x)$df[2]))[2:4]
df.wb.pc &lt;- unlist(lapply(fit.wb.pc, function(x) summary(x)$df[2]))[2:4]
df.wb.pm &lt;- unlist(lapply(fit.wb.pm, function(x) summary(x)$df[2]))[2:4]

df.wh.mc &lt;- unlist(lapply(fit.wh.mc, function(x) summary(x)$df[2]))[2:4]
df.wh.pc &lt;- unlist(lapply(fit.wh.pc, function(x) summary(x)$df[2]))[2:4]
df.wh.pm &lt;- unlist(lapply(fit.wh.pm, function(x) summary(x)$df[2]))[2:4]

df.bh.mc &lt;- unlist(lapply(fit.bh.mc, function(x) summary(x)$df[2]))[2:4]
df.bh.pc &lt;- unlist(lapply(fit.bh.pc, function(x) summary(x)$df[2]))[2:4]
df.bh.pm &lt;- unlist(lapply(fit.bh.pm, function(x) summary(x)$df[2]))[2:4]

df.2g.10 &lt;- c(df.wb.mc, df.wh.mc, df.bh.mc)
df.2g.20 &lt;- c(df.wb.pc, df.wh.pc, df.bh.pc)
df.2g.21 &lt;- c(df.wb.pm, df.wh.pm, df.bh.pm)

cv.2g.10 &lt;- qt(p=0.025, df=df.2g.10, lower.tail=TRUE)
cv.2g.20 &lt;- qt(p=0.025, df=df.2g.20, lower.tail=TRUE)
cv.2g.21 &lt;- qt(p=0.025, df=df.2g.21, lower.tail=TRUE)

cv.2g &lt;- c(cv.2g.10, cv.2g.20, cv.2g.21)

# Assemble vector of 95% CIs
itt.2g.ci &lt;- as.numeric(out2[,2]) + abs(cv.2g)*cbind(-as.numeric(out2[,3]), as.numeric(out2[,3]))
table_a21_cis &lt;- apply(itt.2g.ci, 1, function(x) paste(&quot;[&quot;, round(x[1],3) ,&quot;, &quot;, round(x[2],3), &quot;]&quot;, sep=&quot;&quot;))
# Round and format results
for(i in 2:ncol(out2)) out2[,i] &lt;- round(as.numeric(out2[,i]),3)
for(i in 5) out2[,i] &lt;- paste(&quot;(&quot;,out2[,i],&quot;)&quot;,sep=&quot;&quot;)
# Attach CIs
table_a21 &lt;- cbind(out2, table_a21_cis)
# Clean up column names
colnames(table_a21) &lt;- c(&quot;Outcome&quot;, &quot;Estimate&quot;, &quot;SE&quot;, &quot;t&quot;, &quot;p-value&quot;, &quot;95% CI&quot;)
# Save unformatted table
write.csv(table_a21, &quot;out_tablea21_SA_ExcludeLD_itt_2g_blockfe_nocovs_UNFORMATTED.csv&quot;, row.names=FALSE)

# display table in Rmd output file
kable(table_a21[1:9,], caption=&quot;**Table A21, Panel I. Monitoring vs. Control**&quot;)</code></pre>
<table>
<caption><strong>Table A21, Panel I. Monitoring vs. Control</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">-0.011</td>
<td align="left">0.054</td>
<td align="left">-0.201</td>
<td align="left">(0.42)</td>
<td align="left">[-0.117, 0.095]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.015</td>
<td align="left">0.047</td>
<td align="left">0.315</td>
<td align="left">(0.624)</td>
<td align="left">[-0.077, 0.107]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.008</td>
<td align="left">0.038</td>
<td align="left">0.2</td>
<td align="left">(0.579)</td>
<td align="left">[-0.066, 0.082]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">-0.047</td>
<td align="left">0.059</td>
<td align="left">-0.794</td>
<td align="left">(0.214)</td>
<td align="left">[-0.163, 0.069]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.022</td>
<td align="left">0.045</td>
<td align="left">-0.501</td>
<td align="left">(0.308)</td>
<td align="left">[-0.11, 0.065]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.009</td>
<td align="left">0.035</td>
<td align="left">-0.252</td>
<td align="left">(0.401)</td>
<td align="left">[-0.079, 0.061]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">-0.075</td>
<td align="left">0.054</td>
<td align="left">-1.389</td>
<td align="left">(0.166)</td>
<td align="left">[-0.182, 0.032]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.037</td>
<td align="left">0.043</td>
<td align="left">-0.856</td>
<td align="left">(0.392)</td>
<td align="left">[-0.123, 0.048]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.016</td>
<td align="left">0.032</td>
<td align="left">-0.509</td>
<td align="left">(0.611)</td>
<td align="left">[-0.08, 0.047]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(table_a21[10:18,], caption=&quot;**Table A21, Panel II. Punitive vs. Control**&quot;)</code></pre>
<table>
<caption><strong>Table A21, Panel II. Punitive vs. Control</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.015</td>
<td align="left">0.056</td>
<td align="left">0.267</td>
<td align="left">(0.605)</td>
<td align="left">[-0.095, 0.125]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.017</td>
<td align="left">0.043</td>
<td align="left">0.389</td>
<td align="left">(0.651)</td>
<td align="left">[-0.068, 0.102]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.027</td>
<td align="left">0.035</td>
<td align="left">0.773</td>
<td align="left">(0.78)</td>
<td align="left">[-0.042, 0.095]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.061</td>
<td align="left">0.059</td>
<td align="left">1.033</td>
<td align="left">(0.849)</td>
<td align="left">[-0.055, 0.176]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.067</td>
<td align="left">0.043</td>
<td align="left">-1.58</td>
<td align="left">(0.057)</td>
<td align="left">[-0.151, 0.016]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">-0.013</td>
<td align="left">0.035</td>
<td align="left">-0.363</td>
<td align="left">(0.358)</td>
<td align="left">[-0.082, 0.057]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">0.042</td>
<td align="left">0.052</td>
<td align="left">0.813</td>
<td align="left">(0.417)</td>
<td align="left">[-0.06, 0.144]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.084</td>
<td align="left">0.042</td>
<td align="left">-2.018</td>
<td align="left">(0.044)</td>
<td align="left">[-0.166, -0.002]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.04</td>
<td align="left">0.034</td>
<td align="left">-1.17</td>
<td align="left">(0.243)</td>
<td align="left">[-0.107, 0.027]</td>
</tr>
</tbody>
</table>
<pre class="r"><code>kable(table_a21[19:27,], caption=&quot;**Table A21, Panel III. Punitive vs. Monitoring**&quot;)</code></pre>
<table>
<caption><strong>Table A21, Panel III. Punitive vs. Monitoring</strong></caption>
<thead>
<tr class="header">
<th align="left">Outcome</th>
<th align="left">Estimate</th>
<th align="left">SE</th>
<th align="left">t</th>
<th align="left">p-value</th>
<th align="left">95% CI</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (White vs. Black)</td>
<td align="left">0.018</td>
<td align="left">0.06</td>
<td align="left">0.302</td>
<td align="left">(0.763)</td>
<td align="left">[-0.1, 0.136]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (White vs. Black)</td>
<td align="left">0.023</td>
<td align="left">0.048</td>
<td align="left">0.469</td>
<td align="left">(0.639)</td>
<td align="left">[-0.072, 0.118]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (White vs. Black)</td>
<td align="left">0.032</td>
<td align="left">0.036</td>
<td align="left">0.895</td>
<td align="left">(0.371)</td>
<td align="left">[-0.039, 0.104]</td>
</tr>
<tr class="even">
<td align="left">Index measure of favorable in-person interactions (White vs. Hispanic)</td>
<td align="left">0.105</td>
<td align="left">0.066</td>
<td align="left">1.58</td>
<td align="left">(0.116)</td>
<td align="left">[-0.026, 0.236]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit callback (White vs. Hispanic)</td>
<td align="left">-0.032</td>
<td align="left">0.051</td>
<td align="left">-0.636</td>
<td align="left">(0.525)</td>
<td align="left">[-0.132, 0.068]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit offer for unit (White vs. Hispanic)</td>
<td align="left">0.008</td>
<td align="left">0.04</td>
<td align="left">0.2</td>
<td align="left">(0.841)</td>
<td align="left">[-0.07, 0.086]</td>
</tr>
<tr class="odd">
<td align="left">Index measure of favorable in-person interactions (Black vs. Hispanic)</td>
<td align="left">0.127</td>
<td align="left">0.059</td>
<td align="left">2.135</td>
<td align="left">(0.034)</td>
<td align="left">[0.01, 0.244]</td>
</tr>
<tr class="even">
<td align="left">Received post-visit callback (Black vs. Hispanic)</td>
<td align="left">-0.055</td>
<td align="left">0.047</td>
<td align="left">-1.18</td>
<td align="left">(0.239)</td>
<td align="left">[-0.147, 0.037]</td>
</tr>
<tr class="odd">
<td align="left">Received post-visit offer for unit (Black vs. Hispanic)</td>
<td align="left">-0.024</td>
<td align="left">0.036</td>
<td align="left">-0.688</td>
<td align="left">(0.492)</td>
<td align="left">[-0.094, 0.046]</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:08 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrr}
##   \hline
## Outcome &amp; Estimate &amp; SE &amp; t &amp; p-value &amp; 95\% CI \\ 
##   \hline
## I. Monitoring vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; -0.011 &amp; 0.054 &amp; -0.201 &amp; (0.42) &amp; [-0.117, 0.095] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.015 &amp; 0.047 &amp; 0.315 &amp; (0.624) &amp; [-0.077, 0.107] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.008 &amp; 0.038 &amp; 0.2 &amp; (0.579) &amp; [-0.066, 0.082] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; -0.047 &amp; 0.059 &amp; -0.794 &amp; (0.214) &amp; [-0.163, 0.069] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.022 &amp; 0.045 &amp; -0.501 &amp; (0.308) &amp; [-0.11, 0.065] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.009 &amp; 0.035 &amp; -0.252 &amp; (0.401) &amp; [-0.079, 0.061] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; -0.075 &amp; 0.054 &amp; -1.389 &amp; (0.166) &amp; [-0.182, 0.032] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.037 &amp; 0.043 &amp; -0.856 &amp; (0.392) &amp; [-0.123, 0.048] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.016 &amp; 0.032 &amp; -0.509 &amp; (0.611) &amp; [-0.08, 0.047] \\ 
##   II. Punitive vs. Control &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.015 &amp; 0.056 &amp; 0.267 &amp; (0.605) &amp; [-0.095, 0.125] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.017 &amp; 0.043 &amp; 0.389 &amp; (0.651) &amp; [-0.068, 0.102] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.027 &amp; 0.035 &amp; 0.773 &amp; (0.78) &amp; [-0.042, 0.095] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.061 &amp; 0.059 &amp; 1.033 &amp; (0.849) &amp; [-0.055, 0.176] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.067 &amp; 0.043 &amp; -1.58 &amp; (0.057) &amp; [-0.151, 0.016] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; -0.013 &amp; 0.035 &amp; -0.363 &amp; (0.358) &amp; [-0.082, 0.057] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; 0.042 &amp; 0.052 &amp; 0.813 &amp; (0.417) &amp; [-0.06, 0.144] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.084 &amp; 0.042 &amp; -2.018 &amp; (0.044) &amp; [-0.166, -0.002] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.04 &amp; 0.034 &amp; -1.17 &amp; (0.243) &amp; [-0.107, 0.027] \\ 
##   III. Punitive vs. Monitoring &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   A. White vs. Black &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Black) &amp; 0.018 &amp; 0.06 &amp; 0.302 &amp; (0.763) &amp; [-0.1, 0.136] \\ 
##   Received post-visit callback (White vs. Black) &amp; 0.023 &amp; 0.048 &amp; 0.469 &amp; (0.639) &amp; [-0.072, 0.118] \\ 
##   Received post-visit offer for unit (White vs. Black) &amp; 0.032 &amp; 0.036 &amp; 0.895 &amp; (0.371) &amp; [-0.039, 0.104] \\ 
##   B. White vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (White vs. Hispanic) &amp; 0.105 &amp; 0.066 &amp; 1.58 &amp; (0.116) &amp; [-0.026, 0.236] \\ 
##   Received post-visit callback (White vs. Hispanic) &amp; -0.032 &amp; 0.051 &amp; -0.636 &amp; (0.525) &amp; [-0.132, 0.068] \\ 
##   Received post-visit offer for unit (White vs. Hispanic) &amp; 0.008 &amp; 0.04 &amp; 0.2 &amp; (0.841) &amp; [-0.07, 0.086] \\ 
##   C. Black vs. Hispanic &amp;  &amp;  &amp;  &amp;  &amp;  \\ 
##   Index measure of favorable in-person interactions (Black vs. Hispanic) &amp; 0.127 &amp; 0.059 &amp; 2.135 &amp; (0.034) &amp; [0.01, 0.244] \\ 
##   Received post-visit callback (Black vs. Hispanic) &amp; -0.055 &amp; 0.047 &amp; -1.18 &amp; (0.239) &amp; [-0.147, 0.037] \\ 
##   Received post-visit offer for unit (Black vs. Hispanic) &amp; -0.024 &amp; 0.036 &amp; -0.688 &amp; (0.492) &amp; [-0.094, 0.046] \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="table-a22-p.a-47-distribution-of-subjects-by-their-perceived-race" class="section level2">
<h2><span class="header-section-number">2.25</span> Table A22 (p. A-47): Distribution of Subjects by their Perceived Race</h2>
<pre class="r"><code># with(dat, table(primary_wht, primary_blk)) # 3 are both black and white
# with(dat, table(primary_wht, primary_hsp)) # 8 are both hispanic and white
# with(dat, table(primary_wht, primary_api)) # 4 are both asian and white
dat$subj_race &lt;- rep(NA, nrow(dat))
dat$subj_race &lt;- ifelse(dat$primary_wht == 1 &amp; dat$primary_blk != 1 &amp; dat$primary_api != 1 &amp; dat$primary_hsp != 1, &quot;White&quot;, dat$subj_race)
dat$subj_race &lt;- ifelse(dat$primary_wht != 1 &amp; dat$primary_blk == 1 &amp; dat$primary_api != 1 &amp; dat$primary_hsp != 1, &quot;Black&quot;, dat$subj_race)
dat$subj_race &lt;- ifelse(dat$primary_wht != 1 &amp; dat$primary_blk != 1 &amp; dat$primary_api != 1 &amp; dat$primary_hsp == 1, &quot;Hispanic&quot;, dat$subj_race)
dat$subj_race &lt;- ifelse(is.na(dat$subj_race), &quot;Other&quot;, dat$subj_race)

tab_pcvdrace &lt;- cbind(table(dat$subj_race), round(table(dat$subj_race)/nrow(dat), 2))
colnames(tab_pcvdrace) &lt;- c(&quot;N&quot;, &quot;Proportion&quot;)
pcvrace &lt;- tab_pcvdrace
kable(tab_pcvdrace, caption=&quot;**Table A22**&quot;, col.names=c(&quot;Number of Subjects&quot;, &quot;Proportion&quot;))</code></pre>
<table>
<caption><strong>Table A22</strong></caption>
<thead>
<tr class="header">
<th></th>
<th align="right">Number of Subjects</th>
<th align="right">Proportion</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Black</td>
<td align="right">75</td>
<td align="right">0.11</td>
</tr>
<tr class="even">
<td>Hispanic</td>
<td align="right">83</td>
<td align="right">0.13</td>
</tr>
<tr class="odd">
<td>Other</td>
<td align="right">157</td>
<td align="right">0.24</td>
</tr>
<tr class="even">
<td>White</td>
<td align="right">338</td>
<td align="right">0.52</td>
</tr>
</tbody>
</table>
<pre><code>## % latex table generated in R 3.4.2 by xtable 1.8-2 package
## % Sat Dec  2 19:31:08 2017
## \begin{table}[ht]
## \centering
## \begin{tabular}{rrr}
##   \hline
##  &amp; N &amp; Proportion \\ 
##   \hline
## Black &amp; 75.00 &amp; 0.11 \\ 
##   Hispanic &amp; 83.00 &amp; 0.13 \\ 
##   Other &amp; 157.00 &amp; 0.24 \\ 
##   White &amp; 338.00 &amp; 0.52 \\ 
##    \hline
## \end{tabular}
## \end{table}</code></pre>
</div>
<div id="figure-a6-p.a-48-estimated-effects-of-monitoring-messaging-on-net-discrimination-levels-relative-to-control-by-the-perceived-race-of-the-landlordbroker" class="section level2">
<h2><span class="header-section-number">2.26</span> Figure A6 (p. A-48): Estimated Effects of Monitoring Messaging on Net Discrimination Levels Relative to Control, by the Perceived Race of the Landlord/Broker</h2>
<pre class="r"><code># monitoring-control
fit_mc_llrace &lt;- do.call(rbind, 
                         lapply(names(table(dat$subj_race)), function(J){
                           x&lt;-rbind(summary(lm(ncb_wb ~ TA1 + factor(block), data=dat, subset=(TA %in% c(0,1) &amp; subj_race==J), weights=ipw10))$coefficients[&quot;TA1&quot;,],
                                    summary(lm(ncb_wh ~ TA1 + factor(block), data=dat, subset=(TA %in% c(0,1) &amp; subj_race==J), weights=ipw10))$coefficients[&quot;TA1&quot;,],
                                    summary(lm(noff_wb ~ TA1 + factor(block), data=dat, subset=(TA %in% c(0,1) &amp; subj_race==J), weights=ipw10))$coefficients[&quot;TA1&quot;,],
                                    summary(lm(noff_wh ~ TA1 + factor(block), data=dat, subset=(TA %in% c(0,1) &amp; subj_race==J), weights=ipw10))$coefficients[&quot;TA1&quot;,]
                           )
                           x&lt;-data.frame(Pair = c(&quot;Net Discrimination\nAgainst Blacks (vs. Whites)&quot;, 
                                                  &quot;Net Discrimination\nAgainst Hispanics (vs. Whites)&quot;, 
                                                  &quot;Net Discrimination\nAgainst Blacks (vs. Whites)&quot;, 
                                                  &quot;Net Discrimination\nAgainst Hispanics (vs. Whites)&quot;),
                                         Outcome = c(&quot;Callback&quot;, &quot;Callback&quot;, &quot;Offer&quot;, &quot;Offer&quot;),
                                         SubjectRace = J,
                                         x,
                                         stringsAsFactors=FALSE)
                           return(x)
                         }))
# table(dat$subj_race[dat$TA %in% c(0,1)])
fit_mc_llrace$SubjectRace &lt;- ifelse(fit_mc_llrace$SubjectRace == &quot;Black&quot;, &quot;Black\n(n=48)&quot;, fit_mc_llrace$SubjectRace)
fit_mc_llrace$SubjectRace &lt;- ifelse(fit_mc_llrace$SubjectRace == &quot;Hispanic&quot;, &quot;Hispanic\n(n=61)&quot;, fit_mc_llrace$SubjectRace)
fit_mc_llrace$SubjectRace &lt;- ifelse(fit_mc_llrace$SubjectRace == &quot;Other&quot;, &quot;Other\n(n=115)&quot;, fit_mc_llrace$SubjectRace)
fit_mc_llrace$SubjectRace &lt;- ifelse(fit_mc_llrace$SubjectRace == &quot;White&quot;, &quot;White\n(n=229)&quot;, fit_mc_llrace$SubjectRace)

fit_mc_llrace$SubjectRace &lt;- factor(fit_mc_llrace$SubjectRace, levels=c(&quot;White\n(n=229)&quot;, &quot;Black\n(n=48)&quot;, &quot;Hispanic\n(n=61)&quot;, &quot;Other\n(n=115)&quot;))

ggplot(fit_mc_llrace, aes(group=SubjectRace, colour=SubjectRace)) + 
  geom_linerange(aes(x=Outcome, ymin=Estimate-1.96*Std..Error, ymax=Estimate+1.96*Std..Error), lwd=1, position=position_dodge(width=1/2)) +
  geom_linerange(aes(x=Outcome, ymin=Estimate-1.64*Std..Error, ymax=Estimate+1.64*Std..Error), lwd=2, position=position_dodge(width=1/2)) +
  geom_point(aes(x=Outcome, y=Estimate), size=3.5, position=position_dodge(width=1/2)) + 
  scale_y_continuous(limits = c(-.5, .5), breaks=seq(-.5, .5, .1)) +
  xlab(&quot;\nNet Discrimination Outcome&quot;) +
  ylab(&quot;\nEstimated Effect of Sending\nMonitoring Message (vs. Control)\n&quot;) +
  geom_hline(yintercept=0, lty=2) + facet_wrap( ~ Pair) +
  scale_colour_brewer(palette = &quot;Dark2&quot;, guide = guide_legend(title = &quot;Perceived Race of Landlord/Broker:&quot;)) +
  theme_bw(base_size=15) + theme(legend.position=&quot;bottom&quot;, legend.key = element_blank())</code></pre>
<p><img src="data:image/png;base64,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" width="1056" /></p>
<pre class="r"><code>ggsave(&quot;out_figurea6_appx_hetfxSUBJRACE_mc.pdf&quot;, width=11, height=7)</code></pre>
</div>
<div id="figure-a7-p.a-49-estimated-effects-of-punitive-messaging-on-net-discrimination-levels-relative-to-control-by-the-perceived-race-of-the-landlordbroker" class="section level2">
<h2><span class="header-section-number">2.27</span> Figure A7 (p. A-49): Estimated Effects of Punitive Messaging on Net Discrimination Levels Relative to Control, by the Perceived Race of the Landlord/Broker</h2>
<pre class="r"><code># punitive-control
fit_pc_llrace &lt;- do.call(rbind, 
                         lapply(names(table(dat$subj_race)), function(J){
                           x&lt;-rbind(summary(lm(ncb_wb ~ TA2 + factor(block), data=dat, subset=(TA %in% c(0,2) &amp; subj_race==J), weights=ipw20))$coefficients[&quot;TA2&quot;,],
                                    summary(lm(ncb_wh ~ TA2 + factor(block), data=dat, subset=(TA %in% c(0,2) &amp; subj_race==J), weights=ipw20))$coefficients[&quot;TA2&quot;,],
                                    summary(lm(noff_wb ~ TA2 + factor(block), data=dat, subset=(TA %in% c(0,2) &amp; subj_race==J), weights=ipw20))$coefficients[&quot;TA2&quot;,],
                                    summary(lm(noff_wh ~ TA2 + factor(block), data=dat, subset=(TA %in% c(0,2) &amp; subj_race==J), weights=ipw20))$coefficients[&quot;TA2&quot;,]
                           )
                           x&lt;-data.frame(Pair = c(&quot;Net Discrimination\nAgainst Blacks (vs. Whites)&quot;, 
                                                  &quot;Net Discrimination\nAgainst Hispanics (vs. Whites)&quot;, 
                                                  &quot;Net Discrimination\nAgainst Blacks (vs. Whites)&quot;, 
                                                  &quot;Net Discrimination\nAgainst Hispanics (vs. Whites)&quot;),
                                         Outcome = c(&quot;Callback&quot;, &quot;Callback&quot;, &quot;Offer&quot;, &quot;Offer&quot;),
                                         SubjectRace = J,
                                         x,
                                         stringsAsFactors=FALSE)
                           return(x)
                         }))
# table(dat$subj_race[dat$TA %in% c(0,2)])
fit_pc_llrace$SubjectRace &lt;- ifelse(fit_pc_llrace$SubjectRace == &quot;Black&quot;, &quot;Black\n(n=59)&quot;, fit_pc_llrace$SubjectRace)
fit_pc_llrace$SubjectRace &lt;- ifelse(fit_pc_llrace$SubjectRace == &quot;Hispanic&quot;, &quot;Hispanic\n(n=59)&quot;, fit_pc_llrace$SubjectRace)
fit_pc_llrace$SubjectRace &lt;- ifelse(fit_pc_llrace$SubjectRace == &quot;Other&quot;, &quot;Other\n(n=112)&quot;, fit_pc_llrace$SubjectRace)
fit_pc_llrace$SubjectRace &lt;- ifelse(fit_pc_llrace$SubjectRace == &quot;White&quot;, &quot;White\n(n=249)&quot;, fit_pc_llrace$SubjectRace)

fit_pc_llrace$SubjectRace &lt;- factor(fit_pc_llrace$SubjectRace, levels=c(&quot;White\n(n=249)&quot;, &quot;Black\n(n=59)&quot;, &quot;Hispanic\n(n=59)&quot;, &quot;Other\n(n=112)&quot;))

ggplot(fit_pc_llrace, aes(group=SubjectRace, colour=SubjectRace)) + 
  geom_linerange(aes(x=Outcome, ymin=Estimate-1.96*Std..Error, ymax=Estimate+1.96*Std..Error), lwd=1, position=position_dodge(width=1/2)) +
  geom_linerange(aes(x=Outcome, ymin=Estimate-1.64*Std..Error, ymax=Estimate+1.64*Std..Error), lwd=2, position=position_dodge(width=1/2)) +
  geom_point(aes(x=Outcome, y=Estimate), size=3.5, position=position_dodge(width=1/2)) + 
  scale_y_continuous(limits = c(-.6, .6), breaks=seq(-.8, .8, .1)) +
  xlab(&quot;\nNet Discrimination Outcome&quot;) +
  ylab(&quot;\nEstimated Effect of Sending\nPunitive Message (vs. Control)\n&quot;) +
  geom_hline(yintercept=0, lty=2) + facet_wrap( ~ Pair) +
  scale_colour_brewer(palette = &quot;Dark2&quot;, guide = guide_legend(title = &quot;Perceived Race of Landlord/Broker:&quot;)) +
  theme_bw(base_size=15) + theme(legend.position=&quot;bottom&quot;, legend.key = element_blank())</code></pre>
<p><img 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" width="1056" /></p>
<pre class="r"><code>ggsave(&quot;out_figurea7_appx_hetfxSUBJRACE_pc.pdf&quot;, width=11, height=7)</code></pre>
</div>
<div id="appendix-e.15-p.a-50-analyses-addressing-spillover-concerns" class="section level2">
<h2><span class="header-section-number">2.28</span> Appendix E.15 (p. A-50): Analyses Addressing Spillover Concerns</h2>
<p>For transparency, we show the code we use to conduct the analyses reported in Appendix E.15. However, we have omitted the raw data files that are used to conduct these analyses (i.e., every scraped ad from Craigslist) because they contain personal identifying information about human subjects.</p>
<pre class="r"><code>setwd(&quot;&quot;) # set to directory containing all scraped Craigslist ads (before sampling)
# dirs1 - subdirectory listing, one per day

phone &lt;- NULL # one row for each ad
phone_nums &lt;- NULL 
for(i in 1:length(dirs1)) {
  
  thisdir &lt;- dirs1[i]
  subdirs &lt;- list.files(thisdir)
  subdirs &lt;- subdirs[which(!grepl(&quot;.csv&quot;, subdirs))]
  
  if(length(subdirs) &gt; 0) {
    
    subphone &lt;- array(NA, length(subdirs))
    entry &lt;- array(NA, length(subdirs))
    for(j in 1:length(subdirs)) { # one subdir per listing each day
      
      listing &lt;- list.files(paste0(thisdir, &quot;/&quot;, subdirs[j]))
      listing &lt;- listing[which(grepl(&quot;.html&quot;, listing))]
      if(length(listing) &gt; 1) { listing &lt;- listing[1] }
      list_source &lt;- readLines(paste0(thisdir, &quot;/&quot;, subdirs[j], &quot;/&quot;, listing))
      
      entry[j] &lt;- paste(thisdir, subdirs[j], sep = &quot;_&quot;)
      subphone[j] &lt;- ifelse(length(which(str_extract_all(list_source, 
                                                         &quot;\\(?\\d{3}\\)?[.-]? *\\d{3}[[:space:]]*[.-]+ *[.-[:space:]]?\\d{4}&quot;,
                                                         simplify = TRUE) != &quot;&quot;)) &gt; 0, 1, 0)
      if(subphone[j] == 1) {
        phone_vec &lt;- str_extract_all(list_source, 
                                     &quot;\\(?\\d{3}\\)?[.-]? *\\d{3}[[:space:]]*[.-]+ *[.-[:space:]]?\\d{4}&quot;,
                                     simplify = TRUE)
        phone_nums &lt;- c(phone_nums, unique(phone_vec[which(phone_vec != &quot;&quot;)])) # keep all phone numbers here (not available)
      }
      
      
    }
    phone &lt;- rbind(phone, cbind(entry, subphone))
    
  }
  
}

phone_df &lt;- as.data.frame(phone)
names(phone_df)[2] &lt;- &quot;hasphonenum&quot;
phone_df$hasphonenum &lt;- as.numeric(as.character(phone_df$hasphonenum))

phone_df
# # A tibble: 85,981 x 2
#             entry hasphonenum
#             &lt;chr&gt;       &lt;dbl&gt;
# 1   2012-04-13_1           1
# 2  2012-04-13_10           1
# 3 2012-04-13_100           0
# 4 2012-04-13_101           1
# 5 2012-04-13_102           1
# 6 2012-04-13_103           1
# 7 2012-04-13_104           0
# 8 2012-04-13_105           1
# 9 2012-04-13_106           0
# 10 2012-04-13_107          1
# # ... with 85,971 more rows


# &gt; length(unique(phone_df$entry))
# [1] 85981
# &gt; sum(phone_df$hasphonenum)
# [1] 47978
# &gt; mean(phone_df$hasphonenum) ### estimated proportion of rental listings requiring contact by phone 
# [1] 0.558007

### probability a subject enters the audit sample
2711 / 85981
# [1] 0.03153022
### probability a subject enters the experiment sample
653 / 85981
# [1] 0.007594701

### estimated percentage of duplicate-subject listings by phone number
# &gt; length(which(duplicated(phone_nums)))/length(phone_nums) # 78.96%

0.7896 * 85981
# [1] 67890.6
85981-67890
# [1] 18091
2711 / 18091
# [1] 0.1498535
653 / 18091
# [1] 0.0360953


### Capture-recapture analysis ###

phone_df &lt;- tbl_df(as.data.frame(phone[which(phone[,2] == &quot;1&quot;),])) # keep only listings with phone numbers
phone_df &lt;- phone_df[which(!duplicated(phone_nums)),] # de-dupe using list of phone numbers from above (not available)

# sample 1 - which are marked

set.seed(9382)

phone_s1 &lt;- sample(phone_df$entry, 1000)
#sum(as.numeric(as.character(phone_s1))) # 538

# sample 2

phone_s2 &lt;- sample(phone_df$entry, 1000)

length(which(phone_s2 %in% phone_s1)) # 95

# estimate pop

1000 / (95/1000) # [1] 10526.32

# using 55.8% est. with phone #, this means:

10526.32 / .558 # 18864.37</code></pre>
</div>
<div id="appendix-e.16-p.a-51-joint-distribution-of-the-number-of-testers-in-matched-trios-who-receive-a-callback-and-an-offer" class="section level2">
<h2><span class="header-section-number">2.29</span> Appendix E.16 (p. A-51): Joint Distribution of the Number of Testers in Matched Trios Who Receive a Callback and an Offer</h2>
<pre class="r"><code>dat$num_cb &lt;- apply(dat[,c(&quot;cb_A&quot;, &quot;cb_B&quot;, &quot;cb_C&quot;)], 1, sum)
dat$num_off &lt;- apply(dat[,c(&quot;off_A&quot;, &quot;off_B&quot;, &quot;off_C&quot;)], 1, sum)

appxe16 &lt;- with(dat, table(num_cb, num_off))

appxe16a &lt;- cbind(appxe16, rowSums(appxe16))
appxe16b &lt;- round(cbind(appxe16, rowSums(appxe16)) / rowSums(appxe16) * 100, 2)

appxe16_table &lt;- list()
for(i in 1:nrow(appxe16a)) {
  appxe16_table[[i]] &lt;- rbind(as.character(round(appxe16a[i,], 0)),
                               paste0(&quot;(&quot;, sprintf(&quot;%.2f&quot;, appxe16b[i,]) ,&quot;)&quot;))
}
appxe16_table &lt;- do.call(rbind, appxe16_table)
appxe16_table &lt;- cbind(c(&quot;Number of Testers Receiving a Callback: 0&quot;, &quot;&quot;,
                         &quot;Number of Testers Receiving a Callback: 1&quot;, &quot;&quot;,
                         &quot;Number of Testers Receiving a Callback: 2&quot;, &quot;&quot;,
                         &quot;Number of Testers Receiving a Callback: 3&quot;, &quot;&quot;),
                       appxe16_table)

colnames(appxe16_table) &lt;- c(&quot;&quot;, paste(rep(&quot;Number of Testers Receiving an Offer:&quot;, 4), 0:3), &quot;Row Totals&quot;)
  
kable(appxe16_table, caption=&quot;**Appendix E.16: Number of Testers in a Matched Trio Receiving an Offer by the Number of Testers in a Matched Trio Receiving a Callback.** Cells report counts with percentages in parentheses.&quot;)</code></pre>
<table>
<caption><strong>Appendix E.16: Number of Testers in a Matched Trio Receiving an Offer by the Number of Testers in a Matched Trio Receiving a Callback.</strong> Cells report counts with percentages in parentheses.</caption>
<thead>
<tr class="header">
<th></th>
<th align="left">Number of Testers Receiving an Offer: 0</th>
<th align="left">Number of Testers Receiving an Offer: 1</th>
<th align="left">Number of Testers Receiving an Offer: 2</th>
<th align="left">Number of Testers Receiving an Offer: 3</th>
<th align="left">Row Totals</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Number of Testers Receiving a Callback: 0</td>
<td align="left">427</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">427</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(100.00)</td>
<td align="left">(0.00)</td>
<td align="left">(0.00)</td>
<td align="left">(0.00)</td>
<td align="left">(100.00)</td>
</tr>
<tr class="odd">
<td>Number of Testers Receiving a Callback: 1</td>
<td align="left">68</td>
<td align="left">75</td>
<td align="left">0</td>
<td align="left">0</td>
<td align="left">143</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(47.55)</td>
<td align="left">(52.45)</td>
<td align="left">(0.00)</td>
<td align="left">(0.00)</td>
<td align="left">(100.00)</td>
</tr>
<tr class="odd">
<td>Number of Testers Receiving a Callback: 2</td>
<td align="left">19</td>
<td align="left">20</td>
<td align="left">20</td>
<td align="left">0</td>
<td align="left">59</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(32.20)</td>
<td align="left">(33.90)</td>
<td align="left">(33.90)</td>
<td align="left">(0.00)</td>
<td align="left">(100.00)</td>
</tr>
<tr class="odd">
<td>Number of Testers Receiving a Callback: 3</td>
<td align="left">5</td>
<td align="left">8</td>
<td align="left">6</td>
<td align="left">5</td>
<td align="left">24</td>
</tr>
<tr class="even">
<td></td>
<td align="left">(20.83)</td>
<td align="left">(33.33)</td>
<td align="left">(25.00)</td>
<td align="left">(20.83)</td>
<td align="left">(100.00)</td>
</tr>
</tbody>
</table>
</div>
</div>




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